Stable Isotopes of Estuarine Fish: Experimental Validations and … · 2012. 11. 1. · research....

Stable Isotopes of Estuarine Fish:

Experimental Validations and

Ecological Investigations

Alexandra Louise Bloomfield

Presented for the degree of Doctor of Philosophy

School of Earth and Environmental Sciences

University of Adelaide, South Australia

October 2011

i

Cover image: Chapman River, Kangaroo Island, November 2010.

ii

Stable Isotopes of Estuarine Fish: Experimental Validations and

Ecological Investigations

Abstract ___________________________________________________________ 1

Declaration _________________________________________________________ 4

Acknowledgements ___________________________________________________ 5

Chapter One: General Introduction ________________________________ 7

Isotope chemistry and terminology ______________________________________ 8

Isotopes and their applications ________________________________________ 10

Sample preparation _________________________________________________ 17

Estuaries __________________________________________________________ 20

Thesis outline _______________________________________________________ 22

Chapter Two: Temperature and diet affect carbon and nitrogen isotopes

of fish muscle: can amino acid nitrogen isotopes explain effects? _____ 27

Abstract ___________________________________________________________ 30

Introduction________________________________________________________ 31

Methods ___________________________________________________________ 36

Results ____________________________________________________________ 43

Discussion _________________________________________________________ 58

Acknowledgements __________________________________________________ 69

Chapter Three: The influence of temperature and elemental

concentration of diet on carbon and nitrogen stable isotopes in fish

muscle, with a test of the concentration dependent mixing model ______ 71

Abstract ___________________________________________________________ 73

Introduction________________________________________________________ 74

Methods ___________________________________________________________ 78

Results ____________________________________________________________ 87

Discussion ________________________________________________________ 107

iii

Acknowledgments __________________________________________________ 116

Chapter Four: Stable isotopes allude to separate ecological niches of two

omnivorous, estuarine fishes ____________________________________ 117

Abstract __________________________________________________________ 119

Introduction_______________________________________________________ 119

Methods __________________________________________________________ 123

Results ___________________________________________________________ 134

Discussion ________________________________________________________ 147


Chapter Five: Fish abundance and recruitment show a subsidy-stress

response to nutrient concentrations in estuaries ____________________ 155

Abstract __________________________________________________________ 157

Introduction_______________________________________________________ 158

Methods __________________________________________________________ 163

Results ___________________________________________________________ 173

Discussion ________________________________________________________ 184


Chapter Six: General Discussion _________________________________ 191

Temperature effects ________________________________________________ 192

Diet effects ________________________________________________________ 194

Ecological applications ______________________________________________ 197

Future directions ___________________________________________________ 200

Conclusion ________________________________________________________ 203

Appendix A: Permission to republish Chapter Two ______________________ 204

Appendix B: Supplementary data for Chapter Two ______________________ 206

Bibliography ______________________________________________________ 207

1

Abstract

Stable isotopes of carbon and nitrogen are commonly used in ecological research

to determine food webs and trace anthropogenic inputs. These applications rely on

understanding isotope signature differences between an animal and its food. When

an animal consumes a food item, or changes diet, it does not instantaneously

reflect the isotope ratios of that food item. The isotopic signature of animal tissue

gradually approaches equilibrium with the isotopic signature of its food, as

molecules are turned over and new food items are assimilated into tissues. Stable

isotope ratios also change between food consumed and animal tissues that are

commonly sampled. The difference in stable isotope ratios between an animal‟s

tissue and the food it consumes is called discrimination. The rate of change, or

tissue turnover, and discrimination of stable isotopes varies among and within

animals, and with environmental factors. I investigated the effects of temperature

and diet on these isotope parameters for two fish species and applied results to

improve determination of autotrophic sources within estuaries.

I studied two common, omnivorous, estuarine fishes found in South

Australia: black bream (Acanthopagrus butcheri) and yellow-eye mullet

(Aldrichetta forsteri). Temperature and diet affected both tissue turnover rates and

discrimination of carbon (δ13C) and nitrogen (δ15N) isotope ratios in fish muscle.

Fish reared at warmer temperatures generally had faster tissue turnover rates and

smaller discrimination factors than fish reared at cooler temperatures. However,

temperature interacted with diet quality to affect δ13C discrimination. Fish fed

diets with low C:N ratios had larger δ13C discrimination at warmer temperatures

than at cooler temperatures. This may be caused by fish catabolising more protein

for energy and therefore being able to store more lipids at cooler temperatures

2

than warmer temperatures. Fish fed diets with high C:N ratios were the opposite,

with larger δ13C discrimination at cooler temperatures than at warmer

temperatures.

Compound-specific δ15N analyses were performed on amino acids from

experimental black bream muscle tissues to see if the change in δ15N of amino

acids could explain the bulk change in δ15N of whole muscle tissue. Some amino

acid δ15N results mirrored those of bulk δ15N analyses suggesting that they may be

non-essential amino acids, although there was large variation among individual

fish.

Wild fish commonly consume more than one dietary item, necessitating

the use of mixing models to determine source contributions to diets. Omnivores

consume animal and plant matter that can differ greatly in their elemental

composition and this can affect the uptake of isotopic signatures from different

food sources. I tested the importance of using elemental concentration in mixing

models by combining two diets with different carbon and nitrogen concentrations

and feeding them to yellow-eye mullet. I compared measured δ13C and δ15N of

fish muscle with predicted values calculated with and without using elemental

concentration. Using elemental concentration in mixing models improved

estimates of predicted isotopic signatures.

The experimentally derived discrimination factors for black bream and

yellow-eye mullet were used to investigate the relative importance of autotrophic

sources to their diets in four estuaries in South Australia. Isotope signatures of

carbon and nitrogen can also be used to investigate ecological niches of animals,

as isotope signatures reflect what an animal has eaten from different habitats and

environments. I expected the isotopic niches of black bream and yellow-eye

3

mullet to overlap, due to their shared environmental tolerances and feeding habits,

as they are commonly found in the same estuaries. However, I found no overlap in

isotopic niches between black bream and yellow-eye mullet. In some estuaries the

autotrophic sources that black bream and yellow-eye mullet relied on were

similar, however, in these estuaries fish appeared to be either feeding at different

trophic levels or were likely not in competition with one another as they were

caught in different areas within estuaries. The separate isotopic niches of black

bream and yellow-eye mullet may be caused by habitat partitioning or

interspecific competition within the estuaries studied.

I used δ15N of black bream muscle to trace anthropogenic inputs of

nutrients across a range of estuaries and related nutrient concentrations of

estuarine waters to black bream abundance and recruitment. Black bream

abundance and recruitment showed subsidy-stress responses to nutrient

concentrations of ammonia, oxidised nitrogen and orthophosphorus, with peaks in

abundance and recruitment occurring at low concentrations. A positive linear

relationship was found between ammonia concentration of estuarine waters and

δ15N of black bream. This suggests that anthropogenic ammonia was being taken

up into the food web, or directly by black bream, and affecting black bream

abundance and recruitment.

In summary, I found environmental factors affected stable isotope

signatures of fish muscle tissue. These results further show how important it is to

quantify isotope parameters for individual species. Future research should focus

on how to quantify influences on isotope signatures that cannot be determined in

the field, such as ration intake, and how to account for these factors in field

studies.

4

Declaration

I, Alexandra Louise Bloomfield certify that this work contains no material which

has been accepted for the award of any other degree or diploma in any university

or other tertiary institution and, to the best of my knowledge and belief, contains

no material previously published or written by another person, except where due

reference has been made in the text.

I give consent to this copy of my thesis when deposited in the University

Library, being made available for loan and photocopying, subject to the

provisions of the Copyright Act 1968.

The author acknowledges that copyright of published works contained

within this thesis (as listed below*) resides with the copyright holder(s) of those

works. I also give permission for the digital version of my thesis to be made

available on the web, via the University‟s digital research repository, the Library

catalogue and also through web search engines, unless permission has been

granted by the University to restrict access for a period of time.

*The research in Chapter 2, is the property of Elsevier and has been

included with permission from the publishers. A full version of the publication

can be found in the Journal of Experimental Marine Biology and Ecology,

Volume 399, pages 48-59.

Signed:

Date:

5

Acknowledgements

I was once told that the most important thing to get right when it comes to doing

your PhD, is your supervisors. They were right. Bronwyn Gillanders, in particular,

has been a source of inspiration and intellectual stimulation for me for many years

now, and I would not have been able to complete this thesis without her support

and understanding. Travis Elsdon was always willing to help me in the

development of ideas and analyses and I am in-debited to him. My other „paper‟

supervisor Sean Connell has taught me many things over the years, although

many of them were not related to my thesis topic, he also continues to inspire me.

Running experiments and keeping animals alive is hard work and cannot

be achieved without the assistance of many. I have to thank Benjamin Walther,

John Stanley, Aaron Cosgrove-Wilke, Tom Barnes, Skye Woodcock, Juan Livore

and Karl Hillyard for their assistance in collecting and keeping fish alive for my

research. Judith Giraldo and Pete Fraser also provided invaluable assistance in

collecting field samples. Other lab members who provided inspiration and office

chit-chat do not go unnoticed and my years as a PhD student would have been

rather dull without them. Thank you Owen Burnell, Chris Izzo, Nic Payne, and

Dan Gorman. Using a mass spectrometer is not easy and I thank Rene Diocares

for his prompt analyses of my samples and good data.

Finally, I have to thank my family who have all been so supportive over

the years. Dinners at Dad‟s and overnight stays since I moved to Victor Harbor

have made such a difference to my ability to complete. The unending support

from my Mum and sister, Tee, have kept me going. Thank you Archie for your

company at home and being the source of greatest distraction in my final year; I

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love your little paws. To my understanding and supportive husband, Ross, words

cannot express what you have given me over these years and I thank you.

Archie „helping‟ me study.

7

Chapter One: General Introduction

Harriet River mouth, Kangaroo Island, October 2008.

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General Introduction

Stable isotopes were once almost exclusively used in the earth sciences. However,

with the advent of on-line isotopic analyses some 20 years ago and improved rates

of analysis, biologists began to take interest in their applications. In recent years

the study of stable isotopes and their applications in biology and ecology have

multiplied, coinciding with the gradual reduction in cost of analyses. Applications

of stable isotopes have even been the subject of entire books (e.g. Lajtha and

Michener, 1994; Dawson and Siegwolf, 2007) and include studying plant and

animal physiology (Farquhar et al., 1989; Carleton et al., 2008), food webs (Rush

et al., 2010), animal movements (Herzka, 2005), human impacts on ecosystems

(Schlacher et al., 2005), and environmental change (Dawson and Siegwolf, 2007).

Although stable isotopes can be used to trace elements and molecules through

ecosystems, and detect change over time, experimental validation of isotope

parameters is needed to improve our interpretations of field studies.

Isotope chemistry and terminology

Isotopes are naturally existing atoms of the same element with different mass.

Stable isotopes are known to be stable and not decay radioactively as unstable

isotopes do. They behave in a similar chemical way to atoms of the same element,

forming the same types of compounds. Stable isotopes exist in natural abundances

in varying proportions to their elemental counterparts. The proportion of stable

isotopes relative to elemental atoms can be measured using a mass spectrometer,

in relation to world-wide standards1, and are expressed as the following:

δXY (‰) = ((R sample/R standard) -1) x 103

1 The international standard for carbon is the Peedee Belemnite and the standard for nitrogen is air (N2).

9

where X is the heavy stable isotope of element Y, measured in parts per thousand

(‰). R is the ratio of the heavy isotope (X) to the light isotope of the element Y

for the sample and the international standard. These proportions, or ratios, of

isotopes can be altered by chemical reactions and biological processes. The

change in isotope ratios caused by chemical reactions is called fractionation and

results in different isotopic ratios or signatures in different compounds.

Fractionation occurs through chemical and kinetic effects on chemical

reactions whereby the heavier isotope is concentrated into a particular compound.

In chemical equilibrium reactions the heavier isotope is generally concentrated

into molecules with the greatest bond strength (Dawson and Brooks, 2001).

Kinetic effects occur in biological reactions and physical processes, such as

diffusion, where the heavier isotopes/molecules move slower than their lighter

counterparts, making them less available for chemical reactions. These two

processes combine together to create varying concentrations of stable isotopes in

different compounds, cells and organisms.

There has been some confusion over the use of the term fractionation in

ecological studies, with a large number of alternatives being used: fractionation,

fractionation factor, apparent fractionation, enrichment, trophic enrichment,

trophic fractionation, discrimination, trophic discrimination, discrimination factor,

and tissue-diet discrimination factor (Martínez del Rio et al., 2009). Martinez del

Rio and Wolf (2005) suggested that the term fractionation be used strictly with

regard to chemical reactions, and the equilibrium and kinetic effects that cause

differences in stable isotope ratios between reactants and products. Martínez del

Rio et al. (2009) argued that trophic fractionation encompasses the difference in

stable isotope ratios between an entire animal and its diet, by viewing an animal

10

as a collection of elements and considering assimilation and excretion as chemical

reactions in which an animal participates. They further proposed that the term

discrimination factor be used when referring to the difference in stable isotope

ratios between an animal‟s tissue and its diet. The stable isotope ratio of an

animal‟s specific tissue (e.g. muscle) is the sum total of many chemical reactions

and physical processes within the animal, which can be influenced by what the

animal is experiencing physiologically (e.g. temperature, dehydration, stress). As

individual tissues are influenced by what happens in the rest of the animal‟s body,

using the term „fractionation‟ would be misleading as it refers to physical and

chemical influences and not necessarily physiological. The term discrimination is

more encompassing for tissue-diet differences and I will use it here, as all

following chapters use isotopic measurements of animal muscle tissue.

Discrimination factors are commonly denoted by Δ, and are calculated by:

ΔXYtissue-diet = δXYtissue – δXYdiet

where X is the heavy stable isotope of element Y (e.g. δ13C), and this will be used

throughout this thesis.

Isotopes and their applications

The elements that are commonly used in stable isotope ecological research are

hydrogen, carbon, nitrogen, oxygen, and sulphur. Hydrogen and oxygen isotopes

are used in studies involving water and have been used, among other things, to

trace migrations of birds and to determine past climatic histories (Hobson, 2007

and references therein). Sulphur isotopes (δ34S) are useful in both pollution and

food web investigations. However analyses of hydrogen and sulphur isotopes are

still relatively expensive and require larger sample amounts to gain good data.

Carbon (δ13C) and nitrogen (δ15N) isotopes, on the other hand, are relatively

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inexpensive to analyse and are abundant in organisms as they provide the building

blocks of life, allowing us to trace nutrients and energy through systems. This

thesis will focus on carbon and nitrogen isotopes only from this point on.

Carbon

Carbon isotopes vary among autotrophs due to different photosynthetic pathways,

ratios of demand to supply of carbon, and the source of inorganic carbon

(Farquhar et al., 1989; Marshall et al., 2007). Plants with C3 photosynthesis (e.g.

mangroves) preferentially select 12C over 13C for photosynthesis by RUBISCO,

resulting in δ13C values of approximately -27 ‰ (Farquhar et al., 1989). Plants

using C4 photosynthesis incorporate carbon dioxide into bundle sheath cells first,

storing it as bicarbonate. The heavier isotope concentrates into bicarbonate

compared to carbon dioxide, which is then used for photosynthesis by RUBISCO.

As 13C is concentrated in the bicarbonate, more is incorporated into plant tissue,

leading to a higher δ13C of approximately -13 ‰ (Farquhar et al., 1989).

Crassulacean Acid Metabolism (CAM) plants fix carbon during the night and

store it as malate before releasing the CO2 to RUBISCO during the day, leading to

large variation in δ13C in these plants (Farquhar et al., 1989; Griffiths, 1992).

Environmental conditions, such as water, light and nutrient availability, can also

affect δ13C of plant matter (Dawson et al., 2002) through diffusion affects from

stomatal opening and plant health.

Photosynthesis of aquatic plants is predominantly through the C3 pathway,

however, δ13C values of aquatic plants can be different to terrestrial C3 plants.

This is due to fractionation of CO2 dissolving in water to form bicarbonate

(predominately in the sea), which preferentially contains more 13C. Freshwater

dissolved inorganic carbon varies in δ13C depending on the source of dissolved

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CO2: carbonate rock weathering, mineral springs, atmospheric CO2, or organic

matter respiration (Fry, 2006). There are also boundary affects on the uptake of

carbon by aquatic plants and algae whereby plants remove dissolved inorganic

carbon from the water surrounding them, which is then replaced by diffusion.

However diffusion rates in water are slower than in air, resulting in supply of

carbon being much lower than demand, giving RUBISCO less molecules to

preferentially select 12C from (Fry, 1996).

Carbon isotopes also vary among algae. Phytoplankton typically has

values of -19 to -24 ‰ due to kinetic effects on fractionation (Fry, 2006).

Macroalgae δ13C values vary due to differential uptake of CO2 and bicarbonate

from surrounding waters (Finlay and Kendall, 2007). Dissolved inorganic carbon

δ13C also varies spatially depending on its source, as mentioned above, which

affects δ13C values of algae.

Nitrogen

Isotopic composition of N2 in the atmosphere is 0 ‰ by definition. The rate of

supply of nitrogen often limits reaction rates, such as plant growth and bacterial

mineralization, resulting in smaller differences in δ15N of nitrogen pools than δ13C

among carbon pools. The limiting rate of supply reduces the possibility of

fractionation, as all nitrogen is consumed and one isotope cannot be preferentially

incorporated over another. This results in δ15N ranging from -10 to 10 ‰ in many

biogeological pools (Fry, 2006). However, there are some distinct patterns of δ15N

in biological pools. Nitrification and denitrification can both lead to substantial

δ15N differences of up to 10 to 40 ‰ in the open ocean (Fry, 2006). A faster loss

of 14N over 15N during particulate N decomposition leads to δ15N increasing with

increasing depth of soil and water (Fry, 2006). However, the most important and

13

widely used change in δ15N is the retention of 15N over 14N by animals through

excretion (Martínez del Rio et al., 2009 and references therein).

Isotopic discrimination

Discrimination of δ15N by animals is on average 3.4 ‰ per trophic level (Post,

2002) and this has been used to determine trophic position or level within a food

web (e.g. Kelleway et al., 2010). However, this is a grand average across a range

of species and tissues, and Δ15N is known to vary among animals as well as

among tissues (DeNiro and Epstein, 1981). Although Δ13C has previously been

accepted to be negligible between an animal and its diet, increasingly it is being

shown to be highly variable (Pinnegar and Polunin, 1999; Gaston et al., 2004;

Caut et al., 2009). Therefore there have been calls for more experiments to

determine discrimination factors for individual species and tissues, for δ15N and

δ13C, that can be applied to field studies (Gannes et al., 1997; Martínez del Rio et

al., 2009). Environmental factors have also been shown to affect discrimination

(e.g. temperature: Bosley et al., 2002; Barnes et al., 2007). Therefore experiments

are needed to determine discrimination factors for species of interest under

relevant environmental conditions.

Tissue turnover

Animals do not instantaneously reflect the stable isotope signatures of their diet.

When an animal consumes a food item it is digested, or broken down into

macromolecules, and absorbed into the blood stream. The macromolecules are

then used in the body for growth and metabolism. Molecules that are assimilated

into cell matter through growth and metabolism will reflect the isotopic signature

of the animal‟s diet. However, the isotopic signature of animal tissue is only

14

diluted by the amount of food consumed when their diet changes. The mass of an

animal is usually much greater than the amount of food eaten, therefore it can take

some time for isotopic signatures to change when diets change. Feed rates usually

depend upon the rates of growth and metabolism and these in turn determine

tissue turnover rates. Therefore it is important to measure tissue turnover rates of

animal tissue as well as discrimination of isotopes in experiments. This is

typically done in diet switching experiments where animals are raised on one diet

and then switched to another diet, of different isotopic signature (e.g. Hobson and

Clark, 1992; Mirón et al., 2006; Guelinckx et al., 2007). The change in isotopes

over time is recorded and when the rate of change levels off, the animal is said to

be in equilibrium with its diet as isotopic signatures are no longer changing.

Ecological applications of stable isotopes

The variation in δ13C of primary producers, δ15N increase with increasing trophic

level, and tissue turnover rates have been used to determine modern and historical

diets (Bocherens et al., 2004; Koch, 2007), to study physiology (Carleton et al.,

2008), track animal migrations (Hobson, 2007), and document land-use change

over time (Martinelli et al., 2007). Nitrogen isotopes have also been used to trace

anthropogenic inputs in aquatic ecosystems, particularly sewage inputs (Schlacher

et al., 2005; Hadwen and Arthington, 2007). Sewage and animal wastes contain

high concentrations of urea, which is hydrolysed to ammonia. The ammonia is

then either lost as gas or is dissolved as ammonium in solution. The gas that is lost

as ammonia is strongly depleted in 15N through kinetic affects, leaving behind

15N-enriched ammonium (Heaton, 1986). Therefore ecosystems that are affected

by sewage inputs have high δ15N values.

15

It has been suggested that stable isotopes of carbon and nitrogen can be

used to investigate ecological niches (Newsome et al., 2007). Ecological niches

are comprised of two sets of dimensions: scenopoetic, those that define the area an

animal lives in, and bionomic, those that define the resources an animal requires

to sustain its existence (Hutchinson, 1957, 1978). Carbon and nitrogen isotopes

reflect resources used by an animal and where they have gained these resources.

Therefore stable isotopes of carbon and nitrogen can be used to investigate

ecological niches, although few studies have capitalised on these attributes to date

(e.g. Genner et al., 1999; Olsson et al., 2009; Quevedo et al., 2009).

Compound-specific isotope analysis

A recent advance in the field of stable isotope ecology is that of compound-

specific analyses of isotopes. It is possible to analyse individual compounds in a

solution by coupling a chromatograph to a mass spectrometer so that isotopic

ratios are measured as each compound is eluted out of the chromatograph. Fatty

acids and amino acids are compounds focused on to date as they are able to be

separated using chromatography, and analysed for δ13C and δ15N (McClelland and

Montoya, 2002; Howland et al., 2003), although not simultaneously yet. The

analysis of compound-specific isotopic signatures of amino acids enables

researchers to determine trophic position of animals without needing to sample

autotrophic sources, a great advantage for open ocean studies where sampling is

more challenging (e.g. McClelland and Montoya, 2002; Hannides et al., 2009;

Lorrain et al., 2009). There is great potential for compound-specific isotope

analyses in the field of physiology as well as ecology, with few experimental

studies published to date.

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Mixing models

Food web studies commonly involve measuring multiple sources, or dietary items,

and a target species for which the diet is to be determined. Linear mixing models

can be used to determine the proportional contributions of two sources to a target

using isotopes from a single element, or for three sources using two elements

(Phillips and Gregg, 2001). However, there are often multiple sources and not

enough elements to analyse to allow linear mixing models to find a unique

solution; where the number of sources can only exceed the number of elements

analysed by one. Linear models also assume that the proportional contribution of

a source is equal for all elements analysed. However, elemental concentration

may vary among the elements analysed within the sample (e.g. typical elemental

concentration of carbon in plant matter is approximately 37 % and nitrogen

concentration is approximately 2 %, Chapter 4). Therefore it seems unlikely that

both elements equally contribute to mixing. Phillips and Koch (2002) described a

concentration dependent linear mixing model that assumed a source‟s contribution

is proportional to its elemental concentration. Few experimental tests have been

done on the importance of elemental concentration in mixing models (Caut et al.,

2008), yet it could play an important role in determining isotopic signatures of

target organisms (Pearson et al., 2003; Mirón et al., 2006).

To improve on linear mixing models when multiple sources are present,

Phillips and Gregg (2003) devised a method where all possible combinations of

sources (0-100 %) are calculated in increments (e.g. 1 %). The combinations that

sum to the target isotopic signatures, within a tolerance to allow for variation (e.g.

0.1 %), are considered feasible solutions. The frequency and range of feasible

solutions can then be determined. Phillips and Gregg (2003) wrote a computer

17

program, IsoSource, to perform the calculations for ecologists. IsoSource has been

extremely popular and much used (e.g. Connolly et al., 2005b; Hadwen et al.,

2007), however it does not take into account the inherent variability in isotope

data, nor does it account for elemental concentration of sources.

Bayesian inference methods can incorporate source and discrimination

variability and calculate proportional contributions as true probability

distributions, unlike IsoSource (Moore and Semmens, 2008; Parnell et al., 2010).

Parnell et al. (2010) recently published an open source R package called SIAR:

Stable Isotope Analysis in R. SIAR uses Bayesian inference methods to analyse

stable isotope data and incorporates all sources of uncertainty within data sets. It

also accounts for elemental concentration of sources, encompassing the known

variability and discrepancies in isotope mixing models.

Sample preparation

There are various protocols that have been used for preparation of ecological

samples for stable isotope analysis. It is widely accepted that samples cannot be

preserved with formalin or ethanol without affecting δ13C or δ15N values (Bosley

and Wainright, 1999) therefore freezing of samples is required for short-term

preservation. However, it is either not clear or there is some debate over other

treatment protocols and when it is appropriate to use them. The two key areas

relate to whether samples should be acidified and whether lipids should be

extracted prior to isotope determination.

Acidification

When using stable isotope analyses to investigate diets we only want to analyse

the carbon and nitrogen sources that are relevant to the diet of the study species.

18

This usually does not include dissolved inorganic carbon, since only organic

carbon is of interest for dietary studies (Carabel et al., 2006). However, when

collecting samples containing calcium carbonate deposits, such as molluscs, some

algae and sediment, there is debate over whether acidification of samples should

be performed to remove the inorganic carbonates, as it can affect δ15N values

(Carabel et al., 2006; Mazumder et al., 2010).

It is generally accepted that samples of animals that do not incorporate an

exoskeleton, such as fish, will not need acidification as there will not be any

dissolved inorganic carbon present. Indeed no significant effect of acidification

has been found on fish tissues (Bosley and Wainright, 1999; Pinnegar and

Polunin, 1999; Carabel et al., 2006). However, animals which are too small to

have their carbonate structures removed before analysis, such as molluscs and

crustaceans, or algae may need to be acidified to remove the inorganic carbon.

Carabel et al. (2006) found a significant affect of acidification on δ13C values of

some plankton samples and sedimentary organic matter but no affect on a

macroalga (Laminaria hyperborea). They also found no affect of acidification on

cephalopod muscle tissue δ13C, but there was a significant effect on crab muscle

tissue and on whole crab samples, although variable results for other invertebrate

samples have been obtained by others (Jaschinski et al., 2008b; Mazumder et al.,

2010). A further decrease in δ13C values was observed when crab muscle and

whole crab samples were washed with deionised water after acidification (Carabel

et al., 2006), leading to recommendations against this practice. Acidification is not

required for tissues that do not incorporate a carbonate structure, however it is

advisable for plants and animals that do, such as molluscs, crustaceans, plankton,

articulated coralline algae and sediment samples.

19

Acidification can affect δ15N values as well as δ13C values and this is not

desirable. Pinnegar and Polunin (1999) found a significant difference in δ15N

values between fish samples that were acidified and those that were not, however

the differences were very small (0.6-0.8‰) and are unlikely to affect trophic

studies greatly. Other studies have failed to detect a difference (Bosley and

Wainright, 1999; Jaschinski et al., 2008b), or found a difference in only a few

species or bulk samples (Carabel et al., 2006; Mazumder et al., 2010). Therefore

acidification of samples needs to be based on specific results of published studies

for similar samples, or separately investigated.

Lipid extraction

Lipids are relatively depleted in 13C compared to proteins and carbohydrates in an

organism (DeNiro and Epstein, 1977; Post et al., 2007) and differential storage of

lipids among tissue types may cause different discrimination values (Focken and

Becker, 1998). Animals that have a high feed rate may also have increased lipid

storage compared to other individuals of the same species (Gaye-Siessegger et al.,

2004b). This may cause greater variation among samples and reduce accuracy of

data interpretation.

There has been some discussion in the literature about when to account for

lipids. Post et al. (2007) recommends to account for lipids when lipid content is

high (when C:N > 3.5 for aquatic animals or > 4 for terrestrial animals) or variable

among consumers and when differences in δ13C values between end members is

less than 10-12 ‰. This is the case for most food web studies. They also

recommend accounting for lipid content in plants, however, in food web studies

this does not necessarily make sense. If an animal consumes a plant they will

assimilate much of what they need, be it lipid or protein or carbohydrate.

20

Therefore it makes no sense to extract lipids from plant samples in food webs

studies as animals may assimilate or metabolise the lipids they consume from

plants and may not discriminate against them. Dietary investigations into the

affect of diet composition on discrimination should help to account for varying

lipid intake and eliminate the need for lipid extraction of plant samples in food

web studies (Gaye-Siessegger et al., 2005).

Similarly to acidification of samples, chemical lipid extraction can affect

the δ15N values of samples (Pinnegar and Polunin, 1999; Trueman et al., 2005;

Post et al., 2007). This has led scientists to explore mathematical relationships of

δ13C and lipid content (McConnaughey and McRoy, 1979). Mathematical

normalization of lipids may be more desirable than chemical extraction to

preserve δ15N values and to reduce sample preparation (Post et al., 2007).

Estuaries

An estuary is defined as “a partially closed coastal body of water that is either

permanently, periodically, intermittently or occasionally open to the sea within

which there is a measurable variation in salinity due to the mixture of seawater

with water derived from on or under the land” by the Natural Resources

Management Act SA (2004). Estuaries are highly complex and diverse

ecosystems that vary in their size, hydrology, salinity, tidal characteristics,

geomorphology, sedimentation and ecosystem energetics (Kennish, 2002;

Gillanders, 2007). They are often made up of a range of habitats with varying

autotrophic sources including saltmarshes, mangroves, seagrass, reefs, paperbark

swamps, non-vegetated habitats and open water (Gillanders, 2007), providing a

range of valuable ecosystem services (Costanza et al., 1997). Estuaries are thought

to provide greater food abundance than the surrounding freshwater and marine

21

environments and therefore can support a high biomass of fish and invertebrates

(Kennish, 2002).

Fish are known to move in and out of estuaries (Elsdon and Gillanders,

2006) and among habitats (Russell and McDougall, 2005) throughout their lives.

Estuaries are thought to act as nurseries for many fish species, providing enhanced

food and shelter (e.g. Shaw and Jenkins, 1992; Levin et al., 1997). However, our

ability to make direct observations of fish in estuaries can be hampered by

turbidity. Estuaries are important habitats for fish, however, determining the

ultimate source of nutrition for fish and their migratory habits can be challenging.

Stable isotope analysis provides a novel way to determine the sources of nutrition

for fish, trophic relationships, migration or settlement of larvae, and pollution

sources in estuaries (Herzka et al., 2002; Melville and Connolly, 2003; Schlacher

et al., 2005; Hadwen et al., 2007).

Estuaries are one of the most heavily impacted ecosystems by human

activities (Kennish, 2002), having been a focus for human settlement and activity

throughout the world. They provide fresh water, fertile floodplains, shipping ports

and abundant seafood (Saenger, 1995), attracting human settlement. Kennish

(2002) argues that nutrient load increases and sewage pollution, as a result of

human settlements, are two of the greatest impacts on estuaries. He further

predicts that estuaries with low fresh water flows, or minimal flushing and

therefore high tendency to retain nutrients, are most likely to be greatly affected

by nutrient increases and recent studies have supported this view (Hadwen and

Arthington, 2007). Unfortunately this also describes estuaries in South Australia

(SA). SA is the driest state in Australia with average annual rainfall of just

236 mm (National Water Commission, 2007), and estuaries are rarely flushed and

22

highly likely to be impacted by nutrient increases and other human activities.

There is a dearth of information on estuaries in South Australia and stable isotope

analyses of fish, to elicit ecological information, can help us fill this gap.

Thesis outline

This thesis summarises my doctoral research on stable isotopes of carbon and

nitrogen in fish and their applications in estuarine research. I used experiments to

determine discrimination factors and tissue turnover rates for my study species,

black bream (Acanthopagrus butcheri (Munro, 1949)) and yellow-eye mullet

(Aldrichetta forsteri (Valenciennes, 1836)), and investigated the affects of

temperature and diet on discrimination and tissue turnover rates. I then used these

discrimination factors to determine which autotrophic sources the fishes relied on

in four estuaries in South Australia, using mixing models. I investigated the

isotopic niches of black bream and yellow-eye mullet in these four estuaries to see

if their niches overlapped. I also used δ15N of black bream muscle across a

broader selection of estuaries to determine if black bream abundance and

recruitment were affected by anthropogenic sources of nitrogen in estuaries.

Specific aims were to:

Determine discrimination factors and tissue turnover rates of δ15N and

δ13C for black bream and yellow-eye mullet.

Investigate the effects of temperature and diet on δ15N and δ13C

discrimination and tissue turnover rates.

Determine if compound-specific analyses of amino acid δ15N can indicate

the causes of δ15N discrimination.

Test the importance of using elemental concentration in mixing models.

23

Determine the relative importance of autotrophic sources to black bream

and yellow-eye mullet in four estuaries using stable isotopes.

Use stable isotopes to determine niche overlap of two omnivorous fishes.

Determine if anthropogenic sources of nitrogen in estuaries are affecting

abundance and recruitment of black bream, by using δ15N to trace human-

influenced molecules.

The following four chapters (2-5) are original data, written as articles for

publication in scientific journals. The first chapter has been published, with the

second chapter currently under review. The last two chapters will be submitted to

journals for peer review and publication shortly. While there may be some

inconsistency among chapters with regards to notation or language, such as using

scientific or common names for fish species, this is due to chapters being

submitted to different journals and the need to adhere to individual journal style.

Tables and figures are imbedded within the text and cited references are listed at

the end of the thesis in the Bibliography. The following is a brief overview of

each chapter:

Chapter 2

Discrimination factors and tissue turnover rates can be affected by temperature

and dietary composition and are known to vary among species. This chapter

describes an experiment done on black bream to determine discrimination factors

and tissue turnover rates, with treatments of temperature and diet composition.

After initial bulk isotopic analyses of δ13C and δ15N, compound-specific δ15N of

amino acids were analysed to see if they could explain the results of bulk δ15N

changes.

24

Chapter 3

Elemental concentration of diet can affect stable isotope incorporation rates and

discrimination factors. This chapter describes an experiment on yellow-eye mullet

to determine discrimination factors and tissue turnover rates for fish fed diets

varying in elemental concentration and reared at different temperatures. The use

of elemental concentration in mixing models was also tested by mixing the diets

that varied in elemental concentration and feeding the mixed diets to fish.

Measured results were compared with predicted values from linear mixing

models, with and without accounting for elemental concentration of diets.

Chapter 4

One of the most common applications of stable isotopes is determining diets or

food webs. However, few studies quantify discrimination factors for study

species, with even fewer accounting for environmental influences on

discrimination factors, such as temperature. This chapter summarises the results of

isotopic analyses of black bream and yellow-eye mullet in four estuaries in South

Australia, using the experimentally derived discrimination factors from Chapters 2

and 3. Autotrophic sources, black bream, and yellow-eye mullet were sampled

within estuaries and analysed for δ13C and δ15N. The mixing model SIAR was

used to determine proportional contributions of autotrophic sources to black

bream and yellow-eye mullet diets. The overlap of isotopic niches of black bream

and yellow-eye mullet in each estuary were also analysed, as these two fishes are

both common and omnivorous and are frequently found in the same estuaries in

high abundance.

25

Chapter 5

Nitrogen compounds that are influenced by human activities, particularly sewage,

commonly show high δ15N signatures. This premise was used to detect human

nutrient inputs in estuaries in South Australia where black bream reside and

reproduce. Black bream recruitment to a metapopulation from estuaries was

determined using otolith chemistry. Black bream abundance and nutrient

concentrations of water were measured for each estuary. Responses of black

bream abundance and recruitment to nutrient concentrations were documented.

Relationships of black bream muscle δ15N with ammonia and oxidised nitrogen

concentrations of estuaries were sought to determine if anthropogenic sources of

nutrients were being taken up into the food web and affecting black bream

recruitment and abundance.

Chapter 6

Chapter 6 is a general discussion of the results of the proceeding chapters, with

future directions given for isotope research.

27

Chapter Two: Temperature and diet

affect carbon and nitrogen isotopes

of fish muscle: can amino acid

nitrogen isotopes explain effects?

Experimental fish samples: Acanthopagrus butcheri.

28

Chapter 2 Preamble

This chapter is a co-authored paper published in the Journal of Experimental

Marine Biology and Ecology, and as such, is written in the plural throughout. It is

included with permission from Elsevier (see Appendix A), and can be cited as:

Bloomfield, A.L., Elsdon, T.S., Walther, B.D., Gier, E.J., Gillanders, B.M., 2011.

Temperature and diet affect carbon and nitrogen isotopes of fish muscle: can

amino acid nitrogen isotopes explain effects? Journal of Experimental Marine

Biology and Ecology 399(1), 48-59.

In this chapter Travis Elsdon, Benjamin Walther, Bronwyn Gillanders and

I developed the experimental design and supplied the funding. Travis Elsdon,

Benjamin Walther and I did the experiment, caring for the fish. I prepared the

samples and assisted with the analyses of bulk isotopic signatures. I further

prepared most of the samples for compound-specific δ15N analyses. Elizabeth

Gier prepared some samples and did the compound-specific δ15N analyses. I did

all of the statistical analyses and wrote the accepted manuscript with input from

all co-authors.

I certify that the statement of contribution is accurate

Alexandra Bloomfield (Candidate)

Signed

29

I herby certify that the statement of contribution is accurate and I give permission

for the inclusion of the paper in the thesis

Professor Bronwyn Gillanders Dr Travis Elsdon

Dr Benjamin Walther Dr Elizabeth Gier

30

Temperature and diet affect carbon and nitrogen isotopes

of fish muscle: can amino acid nitrogen isotopes explain

effects?

Abstract

Stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) are widely used in food

web studies to determine trophic positioning and diet sources. However in order

to accurately interpret stable isotope data the effects of environmental variability

and dietary composition on isotopic discrimination factors and tissue turnover

rates must be validated. We tested the effects of temperature and diet on tissue

turnover rates and discrimination of carbon and nitrogen isotopes in an

omnivorous fish, black bream (Acanthopagrus butcheri). Fish were raised at 16°C

or 23°C and fed either a fish-meal or vegetable feed to determine turnover rates in

fish muscle tissue up to 42 days after exposure to experimental treatments.

Temperature and diet affected bulk tissue δ15N turnover and discrimination

factors, with increased turnover and smaller discrimination factors at warmer

temperatures. Fish reared on the vegetable feed showed greater bulk tissue δ15N

changes and larger discrimination factors than those reared on a fish-meal feed.

Temperature and diet affected bulk tissue δ13C values, however the direction of

effects among treatments changed. Analyses of δ15N values of individual amino

acids found few significant changes over time or treatment effects, as there was

large variation at the individual fish level. However glutamic acid, aspartic acid

and leucine changed most over the experiment and results mirrored those of

31

treatment effects in bulk δ15N tissue values. The results demonstrate that trophic

discrimination for δ15N and δ13C can be significantly different than those typically

used in food web analyses, and effects of diet composition and temperature can be

significant. Precision of compound-specific isotope analyses (0.9 ‰) was larger

than our effect size for bulk δ15N diet effects (0.7 ‰), therefore future

experimental work in this area will need to establish a large effect size in order to

detect significant differences. Our results also suggest that compound-specific

amino acid δ15N may be useful for determining essential and non-essential amino

acids for different animals.

Introduction

Understanding where animals derive their energy and nutrition from is important

for management of ecosystems and reconstructing food web dynamics.

Traditional descriptions of aquatic animal diets have come from feeding

observations or gut-content analysis (e.g. Webb, 1973; Gillanders, 1995; Sarre et

al., 2000; Platell et al., 2006). However, results from these methods may not

reflect the actual source of energy and nutrients assimilated in aquatic food webs,

but rather reflect ingested dietary items at one point in time. Stable isotope ratio

analysis, on the other hand, allows assimilated energy and nutrients to be tracked

back to dietary sources (e.g. Melville and Connolly, 2003; Gaston and Suthers,

2004; Connolly et al., 2005b), providing a more complete and time integrated

description of trophic structures.

Stable isotope ratios have been broadly employed to investigate ecological

processes, such as food web dynamics (Michener and Schell, 1994) and larval

settlement (Herzka, 2005). Isotope ratios of 13C to 12C (expressed as δ13C) and 15N

to 14N (expressed as δ15N) are particularly informative, with δ13C being used to

32

trace primary producers and δ15N being used to determine trophic positioning of

consumers (see Smit, 2001; e.g. Connolly et al., 2005b). The isotopic

discrimination factor, or the difference in isotopic composition between a

consumer‟s tissue and its food (Martínez del Rio et al., 2009), varies among tissue

types (DeNiro and Epstein, 1978, 1981). Bulk isotopic discrimination of δ15N is

generally considered to be 2-4 ‰ for most soft tissues, such as muscle and liver

(DeNiro and Epstein, 1981; Minagawa and Wada, 1984; Post, 2002; McCutchan

et al., 2003) and this has been applied to estimate relative trophic positions of

species and individuals (e.g. Melville and Connolly, 2003; Hadwen and

Arthington, 2007). Bulk isotopic discrimination of 13C has been found to be small

compared to the range in δ13C values in nature (DeNiro and Epstein, 1978;

Peterson and Fry, 1987; Post, 2002; McCutchan et al., 2003) and researchers have

often omitted applying a discrimination factor when identifying baseline

compositions of carbon sources in food webs (e.g. Melville and Connolly, 2003;

Connolly et al., 2005b; Hadwen and Arthington, 2007; Hadwen et al., 2007).

However, bulk isotopic discrimination values of δ13C and δ15N have been found to

vary significantly in both laboratory experiments (e.g. Bosley et al., 2002; Gaston

and Suthers, 2004; Trueman et al., 2005; Barnes et al., 2007; Elsdon et al., 2010)

and field studies (e.g. Connolly et al., 2005a; Mill et al., 2007). Applying

inappropriate and untested bulk isotopic discrimination values could lead to

erroneous estimates of both trophic position and baseline sources of food webs

(McCutchan et al., 2003). This has led to calls for more experiments to refine the

magnitude of bulk isotopic discrimination (Gannes et al., 1997; Robbins et al.,

2005; Martínez del Rio et al., 2009).

33

Tissue isotope turnover rate is the speed at which isotopic signatures of

animal tissues change following a dietary shift to a new food (Herzka, 2005).

Tissue turnover rates also vary among species and among tissue types (e.g. bone

collagen turnover takes longer than muscle DeNiro and Epstein, 1978, 1981;

Tieszen et al., 1983; Hesslein et al., 1993; MacNeil et al., 2006; Guelinckx et al.,

2007), which is thought to relate to the relative activity of metabolism and growth.

More metabolically active tissue has faster turnover rates than tissue that is less

metabolically active (Guelinckx et al., 2007). Likewise, actively growing tissue

has faster turnover rates compared to tissue that is not actively growing, although

this is largely due to dilution effects (Herzka, 2005). Water temperature can affect

the tissue turnover rate of fish as their metabolism and growth slows in colder

water, and temperature also affects isotopic fractionation and subsequently

discrimination factors2 (Bosley et al., 2002; Witting et al., 2004; Perga and

Gerdeaux, 2005; Barnes et al., 2007). The composition of the diet does also affect

the allocation of nutrients and therefore the tissue turnover rate (Focken and

Becker, 1998) and discrimination factor (Gaye-Siessegger et al., 2004a; Gaye-

Siessegger et al., 2006). It is vital that the causes of variation in tissue turnover

rates and discrimination factors are understood in order to accurately interpret

field-collected data on stable isotopes in food webs.

Discrimination of carbon and nitrogen isotopes, and assimilation of

nutrients and energy, may also be dependent on physiological factors including

how elements are sourced: such as carbon from sugars or lipids; nitrogen directly

from proteins in the diet, recycled within the animal or synthesised de novo

2 Note that here we use the term „fractionation‟ to refer to the chemical process where reactant and product isotopic signatures differ; and we use the term „discrimination factor‟ to refer to the difference in isotopic signatures between a consumer‟s tissue and its diet, as Martínez del Rio et al. (2009) recommend.

34

(Hobson et al., 1993; Focken and Becker, 1998; Post et al., 2007), and these are

related to diet quality and intake factors. Bulk tissue nitrogen discrimination is

thought to be largely related to protein intake, with the more protein an animal

eats, the more enriched in 15N it becomes (Vander Zanden and Rasmussen, 2001;

Martínez del Rio et al., 2009; Kelly and Martínez del Rio, 2010) because 14N is

preferentially excreted. The excreted nitrogen comes from catabolism of amino

acids. If an animal is eating a protein rich diet it will catabolise more amino acids

for energy creating more depleted excreta and more enriched tissue (Gannes et al.,

1998 and references therein). However, if an animal is eating a protein poor diet,

or it is fasting, it is forced to manufacture its own amino acids by transamination

using proteins already in the tissue and therefore tissue 15N is still enriched

(Hobson et al., 1993; Gannes et al., 1998 and references therein). Theoretically

though, if an animal is consuming a diet that matches its requirements then δ15N

enrichment would be at a minimum (Robbins et al., 2005) as amino acids would

be used directly, with little catabolism or transamination.

Animals are limited in their ability to manufacture amino acids, and

essential amino acids must be obtained from food. Therefore, δ15N values of

essential amino acids should theoretically be preserved in a food web and provide

a conservative tool for identifying food web dynamics. In practise δ15N of all

essential amino acids may not be conserved up the food chain (McClelland and

Montoya, 2002). However different groupings of amino acids, that contain

essential and non-essential amino acids, may yield the same information and

enable us to determine nitrogen sources and trophic position (Schmidt et al., 2004;

Popp et al., 2007; Hannides et al., 2009; Lorrain et al., 2009; Olson et al., 2010).

McClelland and Montoya (2002) found that the δ15N discrimination of some

35

amino acids (i.e. phenylalanine, glycine, serine, threonine, lysine and tyrosine) by

zooplankton consumers was approximately the same or less than the bulk

discrimination between food and consumer (1.7 ‰ in that study). They also found

that several amino acids were enriched in δ15N by more than the bulk

discrimination; they were enriched by ~3-7 ‰ (i.e. alanine, aspartic acid, glutamic

acid, isoleucine, leucine, proline and valine). It is thought that those amino acids

that remain similar to food sources in δ15N undergo dominant metabolic processes

that neither cleave nor form carbon-nitrogen bonds (Chikaraishi et al., 2007); and

these have been called „source amino acids‟ (Popp et al., 2007). Alternatively

those amino acids that are enriched in 15N undergo metabolic processes that

cleave carbon-nitrogen bonds (Chikaraishi et al., 2007); and these have been

called „trophic amino acids‟ (Popp et al., 2007). This has led to the theory that

some amino acids, which may or may not be essential, can be used to trace the

source of nutrients whilst others can indicate trophic position and therefore

consumer samples alone can be used to define trophic position (Popp et al., 2007).

Seasonal variation in δ15N of amino acids in oceanic zooplankton have

been reported (Hannides et al., 2009) with variation in basal δ15N of inorganic

nitrogen being identified as the reason for the variability. However the extent to

which temperature influences the incorporation and subsequent enrichment of

δ15N in amino acids in animals has not been tested. Although some experimental

work analysing the δ15N values of amino acids has been done, it has focused on

invertebrates (McClelland and Montoya, 2002; Chikaraishi et al., 2009) with little

work on fish (Chikaraishi et al., 2009) and it has been acknowledged that more

laboratory experiments are needed to test the broader applications of compound-

specific isotope analyses of amino acids (Hannides et al., 2009; Martínez del Rio

36

et al., 2009; Naito et al., 2010; Olson et al., 2010). To our knowledge no

manipulative experiments have been done to test if environmental or dietary

factors affect δ15N of amino acids, as they are assumed to be unaffected by these

factors.

To increase our understanding of variation in isotopic discrimination

factors and muscle tissue isotope turnover rates an experiment was done on an

omnivorous fish, Acanthopagrus butcheri. We tested the hypotheses that fish

reared at warmer temperatures would have a faster bulk tissue isotope turnover

rate and a smaller bulk isotopic discrimination compared with fish kept at colder

temperatures. We also tested the hypothesis that fish fed a diet based on fish-meal

will have a faster tissue turnover rate and smaller discrimination than those fed a

diet based on vegetable protein. The δ15N of individual amino acids were further

analysed to elucidate the causes of variation in discrimination factors among

treatments. We assumed that δ15N values of certain amino acids do not change or

fractionate, and therefore record the δ15N value of the sources of amino acids,

while other amino acids are highly fractionated and provide a direct estimate of

trophic level as shown by others (i.e. McClelland and Montoya, 2002; Popp et al.,

2007).

Methods

Fish rearing

Juvenile black bream, A. butcheri, were obtained from a hatchery and acclimated

to either 16°C or 23°C to reflect local winter and summer temperatures (Elsdon et

al., 2009). During acclimation fish were maintained on hatchery feed and were fed

this feed for approximately 100 days before the start of the experiment.

37

Treatments consisted of orthogonal combinations of two temperatures, two diets,

and five rearing periods, making a total of 20 combinations with two replicate

tanks per combination.

Fish were randomly allocated to 40L tanks at densities of 6-10 fish per

tank. At this time twenty fish were sacrificed (day 0) to measure initial mean sizes

(68 ± 2 (SE) mm; standard length and 11.52 ± 0.89 (SE) g; mass), with five of

these being analysed for δ13C and δ15N of fish muscle tissue. A subset of 10 fish

was maintained on the hatchery feed for a further 29 days at 16°C, with five fish

sacrificed after seven days and the remaining five after 29 days, to test if fish

muscle was in an isotopic steady state with the hatchery feed. Of the two feeds

that were used during the experimental phase, one was based on fish-meal with a

high protein content, and the other was vegetable based and had lower protein

content (see Table 2.1). Stable isotope signatures of diets were not artificially

enriched so that diets reflected natural situations and results are applicable to field

studies. We acknowledge that protein quality and quantity have varied

concurrently in this experiment, however we believe that this is a realistic

approach as protein quality and quantity are likely to vary concurrently in nature.

Fish were switched to experimental feeds and reared for 2, 7, 14, 28, and 42 days

with entire tanks being sacrificed on these days, as there were replicate tanks for

each time and treatment combination (total of 40 tanks, with 8 tanks sacrificed

each time). Fish were fed two to three times a day to satiation; no dominance

effects were observed.

38

Table 2.1 Attributes of the hatchery feed and the two feeds used in the

experiment. Proximal composition information is taken from the manufacturers‟

packaging. Note: NA = information not available.

Feed Attribute Hatchery feed Fish-meal feed Vegetable feed

δ13C (mean ‰ ± SE) -20.2 ± 0.1 -21.1 ± 0.0 -22.2 ± 0.1

δ15N (mean ‰ ± SE) 9.6 ± 0.1 8.8 ± 0.2 1.4 ± 0.3

Protein (%) 45 43 28

Fat (%) 22 9 4

Carbohydrates (%) NA 15 NA

Fibre (%) NA 2 7

Ash (%) NA 13 14

Moisture (%) NA 12 12

Unaccounted for (%) 33 6 35

Bulk tissue stable isotope ratios: sample preparation and analysis

Fish were sacrificed, weighed (mass (g)) and measured (standard length and total

length (mm)) before having dorsal muscle samples taken. Muscle samples were

frozen to -80°C, then freeze-dried and ground into a powder using a mortar and

pestle, with bones and scales being removed prior to grinding. Feed samples were

oven dried at 85°C for 96 hrs and ground into a powder using a mortar and pestle.

Lipids were not extracted from any samples; C:N for fish muscle averaged a low

3.52 ± 0.04 (SE) and, as we are only comparing samples within a species and not

across a food web, we decided that lipid extraction would introduce more

variation in δ15N and was unnecessary (Post et al., 2007). Lipids were not

extracted for feed samples as fish consume and metabolise the entire feed,

including the lipids. Samples of fish muscle (n = 5/tank) and feeds (n = 5/diet)

39

were weighed into tin capsules for δ13C and δ15N analyses, which were done on a

SerCon ANCA SL/20-20 continuous flow isotope ratio mass spectrometer.

International and internal laboratory reference materials (EDTA, ammonium

sulphate, glycine, and sucrose) were run every 10 samples for calibration of the

instrument readings. Average precision of the machine was 0.18 ‰ for δ15N

values and 0.29 ‰ for δ13C values (1 SD). Average accuracy was 0.14 ‰ for δ15N

values and 0.36 ‰ for δ13C values.

Amino acid analysis

Fish muscle samples (n = 3/treatment group) were randomly picked from within a

subset of treatment groups and prepared for analysis of δ15N values of amino

acids. Fish from the start of the experiment (time = 0) and fish from each of the

treatment groups (n = 4 groups) at the end of the experiment (time = 42 days)

were analysed. Fish from days 7, 14 and 28 reared on the vegetable feed at 23°C

were also analysed because the bulk δ15N values showed the greatest nitrogen

discrimination factor and change over time (see results). Within each treatment

group at least one fish from each replicate tank was chosen for analysis. The three

feeds (hatchery, vegetable, and fish-meal) were also prepared and analysed for

δ15N of amino acids.

Acid hydrolysis and derivatization

Samples were prepared similar to Hannides et al. (2009) for amino acid hydrolysis

and derivitization with minor deviations. Sub-samples of approximately 5-10 mg

of freeze-dried fish muscle and 20 mg of oven dried fish food were weighed into

8 mL screw-cap glass vials for acid hydrolysis. Each vial had 0.5 mL of 6N HCl

acid (sequanal grade, constant boiling) added. Vials were flushed with N2, sealed

40

with Teflon-lined caps and heated to 150°C for 70 min to hydrolyse proteins into

amino acids. The vials were cooled and then evaporated until dry at 55°C under a

stream of N2. The dried hydrolysate was then re-dissolved in 1 mL of 0.01N HCl

acid and filtered through low protein-binding, 0.22 µm, hydrophilic filters. Vials

were rinsed with a further 1 mL of 0.01N HCl and filtered. The hydrolysate was

purified further through 5 cm of a cation-exchange column made of Dowex

50WX8-400 ion exchange resin (Metges and Petzke, 1996) suspension using

0.01N HCl acid, in a Pasteur pipette, blocked with glass wool. Amino acids were

eluted through the column by adding repeated rinses of 2N NH4OH totalling 4 mL

in volume. The elutate was evaporated until dry at 80°C under a stream of N2. The

samples were re-acidified with 0.5 mL of 0.2N HCl acid, vials were flushed with

N2 and heated to 110°C for 5 min.

Amino acids were derivatized by esterification of the carboxyl terminus

and trifluoroacetylation of the amine group (Macko et al., 1997; Popp et al.,

2007). Samples were dried under a stream of N2 at 55°C. To each sample, 2 mL of

4:1 isopropanol:acetyl chloride was added, then vials were flushed with N2 and

heated to 110°C for 60 min. After the vials cooled, excess solvent was evaporated

to dryness under a stream of N2 at 60°C. To each sample, 800 µL of a 3:1

methylene chloride:trifluoracetic anhydride (TFAA) was added and heated to

100°C for 15 min, with the vial caps on, to acylate amino acids. Vials were cooled

and the liquid evaporated to dryness under N2.

Samples were further purified by solvent extraction through re-dissolving

them in 2 mL of phosphorus-buffer (KH2PO4 + Na2HPO4 in ultra pure water,

pH 7) and 1 mL of chloroform, mixed vigorously for 60 s and centrifuged at 600 g

for approximately 2 min (Ueda et al., 1989). The chloroform layer containing the

41

acylated amino acid esters was extracted into a clean vial and 1 mL of chloroform

was further added to the remaining phosphorus-buffer mixture. The phosphorus-

buffer and chloroform mixture was mixed vigorously and centrifuged again. The

chloroform layer was extracted and added to the first extraction. The chloroform

solution was evaporated until dry under a stream of N2 at room temperature.

Another 800 µL of 3:1 methylene chloride:trifluoracetic anhydride (TFAA) was

added to each sample to ensure complete derivatization and heated to 100°C for

15 min, with vial caps on. Vials were stored in this solution at < 4°C until they

were analysed.

Compound-specific stable nitrogen isotope analysis

The δ15N values of amino acid derivatives were analysed using a Delta XP mass

spectrometer interfaced to a Trace gas chromatograph through a GC-C III

combustion furnace at 980°C, reduction furnace at 680°C, with a liquid nitrogen

cold trap. All samples were evaporated until dry at room temperature under a

stream of N2 and then re-dissolved in 100 to 1,000 µL ethyl acetate before

analysis. Samples were co-injected with 0.5 µL of standards (norleucine and

aminoadipic acid) (split/splitless 10:1 split ratio) at 180°C injector temperature

under constant helium flow rate for 2 mL min-1. The column oven was initially

held at 50°C for 2 min, ramped to 190°C at 8º min-1, and then to 280°C at

10º min-1, and finally held at 280°C for 10 minutes. Samples were analysed

multiple times and calibrated against standards. This method gave δ15N

measurements for alanine, glycine, threonine, valine, serine, leucine, isoleucine,

proline, aspartic acid, glutamic acid, phenylalanine, and lysine. Note that

asparagine and glutamaine are converted to aspartic acid and glutamic acid

respectively during acid hydrolysis and that they are included in δ15N

42

measurements of these amino acids. Tyrosine was present and δ15N values were

measured in some samples, but not all, therefore it was excluded from analyses.

Average precision was 0.94 ‰ (1 SD) and average accuracy was 0.30 ‰.

Statistical analysis

One-way ANOVAs were performed on δ13C and δ15N values of fish fed the

hatchery feed for varying periods of time. Four factor ANOVAs, with main

factors of temperature, diet, and time (excluding time 0) (all fixed factors) and

tank as a nested random factor within treatments, were used to investigate

differences in δ15N and δ13C values, lengths, and weights of fish among

treatments. Isotopic discrimination factors of fish reared for 42 days were

calculated by deducting the respective feed δ15N and δ13C values from individual

fish values. Discrimination factors of fish reared for 42 days were analysed using

a three factor ANOVA of diet and temperature treatments (fixed factors), with

tank as a random nested factor within treatments. Post-hoc comparisons were

done using Student-Newman-Keuls (SNK) tests. Exponential decay relationships

of δ15N and δ13C values over time were investigated based on the equation of

Guelinckx et al. (2007):

δ(t) = δfinal + (δinital – δfinal)exp(vt)

Where δ(t) is the value at the time in question; δfinal is the final value of tissue after

reaching a steady state; δinital is the value at the beginning of the experiment; t is

the time fish had been reared on experimental feeds (days); v is a measure of the

turnover rate and has units of t-1.

Permutation multivariate analysis of variance (PERMANOVA)

(Anderson, 2001) was performed to investigate differences in the nitrogen

isotopic composition of amino acids in feeds. Data were not transformed,

43

resembled using Euclidean similarity distance matrices, and permutations were

unrestricted. Individual ANOVAs on isotopic results of amino acids in fish were

done, as some amino acids were expected to change and others were expected to

remain unchanged from treatment effects. Two-way, nested ANOVAs on the δ15N

values of amino acids in fish were done on the samples generated at the end of the

experiment (42-day samples) to see if there was an effect of diet or temperature on

the δ15N of amino acids. One-way, nested ANOVAs were done on amino acid

δ15N from the initial fish and fish fed the vegetable feed at 23°C over time to see

if δ15N values of amino acids changed during the experiment. Replicate injections

of individual fish were nested within fish, which were nested within tank and

treatments (diet × temperature for two-way ANOVA; time for one-way ANOVA)

to account for analytical variation. The nitrogen discrimination factors, i.e. the

difference in δ15N values between tissue and diet for each amino acid, was

calculated from mean values for each feed being deducted from mean values for

each fish. The discrimination factors were then analysed by a three-factor

PERMANOVA, as above, with diet and temperature as fixed factors and tank as a

random factor nested within them.

Results

There was large size variation among fish sampled initially (53 – 82 mm SL; 4.77

– 18.5 g). However, data of average sizes of fish reared for 2 and 42 days,

grouped by treatment, show that fish grew over the experiment (Table 2.2) and

fish size (length and mass) was affected by rearing time (Table 2.3). Fish mass

was not affected by treatments of diet, temperature, or tank (Table 2.3). The

standard lengths of fish were affected by temperature (Tables 2.2 & 2.3), with fish

reared at 23°C being longer on average than fish reared at 16°C.

44

Table 2.2 Average size of analysed fish (n = 5/treatment; mass and standard

length (SL)) for fish reared for 2 or 42 days and fed either fish-meal or vegetable

feed and reared at 16°C or 23°C.

Diet Temperature Day Mass (mean ± SE, g) SL (mean ±SE, mm)

Fish-meal 16°C 2 10.27 ± 1.26 65 ± 3

42 10.68 ± 1.53 66 ± 4

23°C 2 11.83 ± 1.97 68 ± 3

42 13.94 ± 1.19 74 ± 2

Vegetable 16°C 2 8.89 ± 0.81 64 ± 3

42 11.74 ± 0.95 69 ± 2

23°C 2 10.84 ± 0.59 68 ± 1

42 11.06 ± 1.35 71 ± 2

45

Table 2.3 Four factor analysis of variance (ANOVA) of treatment effects on fish

mass and standard length. Bolded numbers indicate significant effect of treatment

(p < 0.05). Data were not transformed.

Source of Variation

df

Mass Standard Length

MS p MS p

Day 4 94.480 0.004 401.950 0.001

Diet 1 48.637 0.103 60.082 0.283

Temperature 1 59.999 0.067 1017.700 0.001

Day × Diet 4 25.463 0.200 63.364 0.397

Day × Temperature 4 28.625 0.195 90.441 0.232

Diet × Temperature 1 29.517 0.178 41.628 0.416

Day × Diet × Temperature 4 40.441 0.074 115.420 0.133

Tank (Diet × Temp. × Day) 20 15.915 0.705 56.484 0.836

Residual 212 19.681 81.413

Total 251

Tissue isotope turnover

Temperature affected tissue isotope turnover rate, with a significant interaction

between temperature and day for δ15N values (see Table 2.4, Fig. 2.1a). Fish

reared at 16°C appeared to have reached a steady state with their new feeds after

14 days, as there were no significant differences in δ15N values among fish reared

for 14, 28, and 42 days (in post-hoc tests when pooled across diets) (Fig. 2.1a).

Fish reared at 23°C, however, appeared to have taken much longer to equilibrate

with the isotopic values of their feeds. In fact fish may not have reached a steady

state or isotopic equilibrium during the experiment, with significant differences

46

detected among most rearing times when pooled across diets. It was not possible

to fit exponential equations to the 23°C treatments with reasonable r2 values,

therefore no regression lines are shown (Fig 2.1a). Diet had a significant effect on

δ15N values of fish tissue (Table 2.4, Fig. 2.1a), with fish fed the fish-meal feed

having higher δ15N values than those fed the vegetable feed. Fish maintained on

the hatchery feed did not change significantly in δ15N or δ13C over time (F2,12 =

0.54, p = 0.60; F2,12 = 0.14, p = 0.87 respectively).

Table 2.4 Four factor ANOVA of treatment effects on δ15N and δ13C. Bolded

numbers indicate significant effect of treatment (p < 0.05). Data were not

transformed.

Source of Variation

df

δ15N δ13C

MS p MS p

Day 4 9.487 0.001 1.182 0.001

Diet 1 8.989 0.001 0.001 0.896

Temperature 1 4.031 0.002 0.181 0.092

Day × Diet 4 0.252 0.455 0.217 0.027

Day × Temperature 4 2.752 0.001 0.116 0.155


Day × Diet × Temperature 4 0.257 0.447 0.190 0.034

Tank (Diet × Temp. × Day) 20 0.267 0.373 0.060 0.533

Residual 157 0.248 0.063

Total 196

47

Figure 2.1 Average a) δ15N and b) δ13C (‰ ± SE) of fish muscle tissue from fish

reared at 16°C and fed vegetable (■) or fish-meal feed (▲) and fish reared at

23°C and fed vegetable (□) or fish-meal feed (Δ) over 42 days. Note: as there was

no effect of tank (Table 2.4) tanks have been pooled; exponential decay curves are

fitted to 16°C treatments for δ15N only.

Day after diet switch

0 10 20 30 40

δ13C

-18.6

-18.4

-18.2

-18.0

-17.8

-17.6

-17.4

-17.2

-17.0b)0 10 20 30 40

δ15N

12.0

12.5

13.0

13.5

14.0

14.5

15.0

15.5a)

y=14.26+0.50e(-0.05x)

r2=0.64

y=13.57+1.03e(-0.07x)

r2=0.78

48

Fish tissue δ13C values changed during the experiment and were affected

by both diet and temperature treatments, with significant interactions between

day, diet and temperature (Table 2.4, Fig. 2.1b). Tissue δ13C values initially

increased or stayed the same for the first 14 days overall, with fish fed the

vegetable feed at 16°C increasing the most, although the increase was still very

small (<0.3 ‰) and within the bounds of our precision. Tissue δ13C values then

generally decreased over the last 28 days of the experiment, with fish fed the

vegetable feed at 23°C decreasing the most.

Discrimination factor

Diet affected δ15N and δ13C discrimination (Table 2.5, Figs 2.2a and b), and fish

fed the vegetable feed had a greater discrimination factor than fish fed the fish-

meal feed. Temperature affected δ15N discrimination with fish reared at 16°C

having a greater discrimination of δ15N than those reared at 23°C. Although a tank

effect was detected in the ANOVA of δ15N discrimination values (Table 2.5),

significant differences were only detected between one pair of tanks and this did

not alter main effects. This pair of tanks did not vary in fish size (mass: p = 0.19

(2-tailed T-test); SL: p = 0.27 (2-tailed T-test)).

An interaction between diet and temperature treatments was detected for

δ13C discrimination (Table 2.5). The directions of temperature effects on δ13C

discrimination switched depending on which feed fish were fed. Fish fed the

vegetable feed had a greater δ13C discrimination factor at 16°C than at 23°C

(Fig. 2.2b), however, fish fed the fish-meal feed had a significantly greater δ13C

discrimination factor at 23°C than at 16°C (Fig. 2.2b).

49

Table 2.5 Three factor ANOVA of treatment effects on δ15N and δ13C

discrimination (Δ) (tissue-diet). Bolded numbers indicate significant effect of

treatment (p < 0.05). Data were not transformed.

Source of Variation

df

Δ15N Δ13C

MS p MS p

Diet 1 418.500 0.001 6.769 0.003

Temperature 1 13.324 0.020 0.534 0.162


Tank (Diet × Temp.) 4 1.020 0.024 0.154 0.125

Residual 30 0.319 0.074

Total 37

50

Figure 2.2 Average discrimination factor (Δ) of a) δ15N and b) δ13C (tissue-diet)

(‰ ± SE) after 42 days of rearing on either vegetable or fish-meal feed at either

16°C or 23°C.

Fish-meal feed Vegetable feed

Δ15 N

tissu

e-di

et

0

2

4

6

8

10

12

14a)

Feed

Fish-meal feed Vegetable feed

Δ13 C

tissu

e-di

et

0

1

2

3

4

5b)

16ºC 23ºC

51

Amino acids

The δ15N composition of amino acids in the three feeds were significantly

different from each other (PERMANOVA F2,6 = 411.02, p < 0.05) (Fig. 2.3). The

vegetable feed generally had lower δ15N values for most amino acids compared to

both the hatchery and fish-meal feeds. The exception to this pattern was threonine,

for which the δ15N values of the hatchery and vegetable feeds were more similar

and enriched compared to the fish-meal feed (Fig. 2.3).

Figure 2.3 Average amino acid and bulk δ15N values (‰ ± SE) of the three feeds

used during the experiment.

-10

-5

0

5

10

15

20

25

Amino acid and bulk

δ15N

Hatchery feedFish-meal feedVegetable feed

52

The differences in δ15N values of amino acids between experimental feeds

(vegetable and fish-meal) were not reflected in fish after 42 days (Fig. 2.4). There

was no significant effect (p > 0.05) of diet or temperature on the δ15N values of

amino acids in fish reared for 42 days, except for glutamic acid which was

affected by diet treatments (p = 0.015). Tissue δ15N values of leucine, aspartic

acid, and glutamic acid had similar patterns of treatment effects after 42 days to

bulk tissue δ15N (Fig. 2.4). The δ15N for fish fed the fish-meal feed were higher

than fish fed the vegetable feed. Tissue δ15N of leucine, aspartic acid, and

glutamic acid were also lower for fish kept at 23°C than those kept at 16°C. There

was a significant effect at the individual fish level for every amino acid analysed

(p < 0.05). This led us to investigate the variance components as per Kingsford

and Battershill (1998). Diet and temperature treatments were grouped into one

treatment of four factors because there were no significant effects of treatments,

except for glutamic acid. Variance associated with individual fish explained at

least 30 % of the total variation (Table 2.6). Two amino acids, glycine and lysine,

had the greatest variation among replicate injections for individual fish, although

variation at the fish level was still greater than 30 %.

Fish fed the vegetable feed and reared at 23°C showed no significant

change through time in δ15N of amino acids (p > 0.05), except for glycine

(p < 0.05, Fig. 2.5). There was an overall trend of decreasing δ15N values with

time for most amino acids: alanine, glycine, valine, leucine, isoleucine, proline,

aspartic acid, and glutamic acid (see Fig. 2.5 and Appendix B). However, there

was large variation within day groups for most amino acids except glycine. All

ANOVAs found a significant effect of individual fish on δ15N of amino acids

(p < 0.01), similar to the above analyses for fish after 42 days. Variance

53

components indicated that individual fish explained at least 30 % of total variation

for all amino acids and it was the greatest source of variation, except for glycine,

which had the greatest variance associated with replicate injections (Table 2.7).

Figure 2.4 Average δ15N (‰ ± SE) of amino acids and bulk in fish muscle from

fish reared at 16°C and fed vegetable (■) or fish-meal feed (▲) and fish reared at

23°C and fed vegetable (□) or fish-meal feed (Δ) after 42 days.

-20

-10

0

10

20

30

Amino acid and bulk

δ15N

54

Table 2.6 Variance components (%) of experimental treatments (diet × temperature), tank, fish and replicate injections for δ15N of

amino acids in fish muscle after 42 days being fed two different feeds (fish-meal or vegetable) at two temperatures (16°C or 23°C).

Ala = alanine, Gly = glycine, Threon = threonine, Val = valine, Ser = serine, Leu = leucine, Isoleu = isoleucine, Pro = proline, Aspa =

aspartic acid, Glu = glutamic acid, Phenyl = phenylalanine, Lys = lysine. Bolded numbers highlight the source of the greatest

variation per amino acid.

Amino Acid Ala Gly Threon Val Ser Leu Isoleu Pro Aspa Glu Phenyl Lys

Experimental treatments (Diet × Temp.) 0 0 0 0 0 5.60 0 0 0 28.51 0 0

Tank (D × T) 36.73 33.25 39.14 28.57 21.42 14.35 31.37 8.44 31.50 0 0 15.16

Fish(Tank(D × T)) 31.18 32.60 43.07 42.58 64.70 47.29 55.80 61.85 46.30 52.28 85.07 39.12

Residual (replicate injections) 32.08 34.16 17.80 28.85 13.88 32.76 12.83 29.71 22.20 19.21 14.93 45.72

55

Figure 2.5 Average δ15N of a selection of amino acids (‰ ± SE) in fish muscle

from fish fed the vegetable feed at 23°C over time, including initial fish (day = 0).

Amino acids on the left are „source‟ amino acids, those on the right are „trophic‟

amino acids as per Popp et al. (2007). Note all graphs have y-axes that span 7‰,

although they are not the same values.

δ15N

Glycine

0 7 14 21 28 35 423

4

5

6

7

8

9

10

Phenylalanine

0 7 14 21 28 35 425

6

7

8

9

10

11

12

Lysine

0 7 14 21 28 35 424

5

6

7

8

9

10

11

Aspartic Acid

0 7 14 21 28 35 42

19

20

21

22

23

24

25

Glutamic Acid

0 7 14 21 28 35 4221

22

23

24

25

26

27

28

Day after diet switch

Leucine

0 7 14 21 28 35 4221

22

23

24

25

26

27

28

56

Table 2.7 Variance components (%) of time (day), tank, fish and replicate injections for δ15N of amino acids in fish muscle from fish

fed a vegetable feed, reared at 23°C over time. Abbreviations are the same as for Table 2.6. Bolded numbers highlight the source of

the greatest variation per amino acid.

Amino Acid Ala Gly Threon Val Ser Leu Isoleu Pro Aspa Glu Phenyl Lys

Day 8.07 23.31 0 10.10 0 1.68 0 0 0 4.45 6.85 0

Tank(Day) 0.48 0 0 0 0 0 30.26 13.71 8.11 0 0 0

Fish(Tank(Day)) 70.16 34.72 82.73 59.51 79.10 82.58 44.41 58.34 85.46 81.55 70.66 87.56

Residual (replicate injections) 21.28 41.97 17.27 30.39 20.90 15.74 25.33 27.94 6.43 14.00 22.50 12.44

57

There was a significant affect of diet on the discrimination of δ15N for amino

acids (PERMANOVA; F2,12 = 62.663, p = 0.001) (Fig. 2.6). The vegetable feed

had the largest discrimination factors for nearly all amino acids, with the

exception of threonine for which the fish-meal feed had the largest discrimination

factor.

Figure 2.6 Average amino acid δ15N discrimination factors (tissue-diet) (Δ ‰ ±

SE) for fish sacrificed at the beginning of the experiment (hatchery feed), and

those sacrificed after 42 days of rearing on vegetable or fish-meal feeds. Note:

results are pooled over temperature as there was no effect of temperature.

-15

-10

-5

0

5

10

15

20

25

Hatchery feedFish-meal feedVegetable feed

Amino acid

Δ15

Ntis

sue-

diet

Bulk isotopic discrimination for vegetable feed

Bulk isotopic discrimination for hatchery and fish-meal feed

58

Discussion

Bulk tissue δ15N and δ13C

Both temperature and diet affected δ13C and δ15N values of fish muscle tissue.

Fish reared at warmer temperatures had faster turnover of δ15N than fish reared at

cooler temperatures, which was expected. However, unexpected variation was

found for when fish reached a steady state or isotopic equilibrium with their diet

for the two temperature treatments. Fish reared at warmer temperatures do not

appear to have reached a steady state after 42 days, whereas those reared at cooler

temperatures appear to have reached a steady state after 14 days. This contrasts

with findings of others (Bosley et al., 2002; Witting et al., 2004), who generally

found faster tissue turnover rates and shorter times to reach a steady state for fish

at warmer temperatures than fish at cooler temperatures. Isotopic turnover is due

to the combined processes of growth dilution and metabolic reworking (Fry and

Arnold, 1982; Hesslein et al., 1993; Herzka et al., 2002) and it is generally

considered that growth is the main contributor to isotope turnover of muscle for

growing poikilotherms (Fry and Arnold, 1982; Bosley et al., 2002; Witting et al.,

2004; Trueman et al., 2005; Carleton and Martínez del Rio, 2010). However,

metabolic activity also contributes to isotope turnover to varying degrees.

Tarboush et al. (2006) found that metabolism contributed over 65 % to isotope

turnover in young adult fish indicating that the contribution of metabolism to

isotope turnover may vary with age or growth rates of fish. In our experiment, fish

reared at 16°C on average were smaller than fish reared at 23°C and did not grow

as fast, if at all. Therefore the rates of change detected at 16°C may be more

similar to turnover rates due to metabolism than rates due to growth and

metabolism combined (Carleton and Martínez del Rio, 2010). If this is the case,

59

fish reared at 16°C appear to have retained an historical dietary signature from the

previously consumed diet. In contrast, fish reared at 23°C grew faster leading to

more rapid dilution of their historical dietary signature through the addition of

bulk tissue. However in order to reach a steady state, or isotopic equilibrium, at

23°C the experimental period for A. butcheri would need to be longer. These

results support the theory that isotopic signatures only reflect food consumed

while animals are growing (Perga and Gerdeaux, 2005; Carleton and Martínez del

Rio, 2010). Fish are known to grow faster in warmer waters, and therefore

isotopic signatures of wild fish tissue may be weighted towards diets consumed

during growth periods in summer seasons (Perga and Gerdeaux, 2005; Carleton

and Martínez del Rio, 2010).

Some previous researchers have identified durations required for juvenile

and mature fish to reach an isotopic steady state with their diet that exceed the six

weeks of our experimental period. Trueman et al. (2005) found that a 300 %

increase in mass of one year old Atlantic salmon (starting at an average weight of

48.5g) was required to achieve complete muscle tissue turnover, which took eight

months. Zuanon et al. (2007) reared Nile tilapia fingerlings (starting at 3.5g) for

nearly two months to reach an isotopic steady state, after which fish had more

than doubled their mass. Partridge and Jenkins (2002) found A. butcheri to double

in mass over a period of approximately one month, for fish of similar size to those

used in our experiment. Therefore it was expected that fish in our experiment

would have at least doubled in mass over six weeks and reached an isotopic

steady state. However Partridge and Jenkins (2002) fed fish supplemental fresh

food (prawns, muscles, and whitebait) in addition to commercial aquaculture feed

and we were not able to do this as it would interfere with our isotope results.

60

Unfortunately, A. butcheri in this experiment (starting at an average mass of

11.52 g) increased in size by less than 50 % and although it was evident that fish

grew there was size variation among fish from the outset. It would have been

desirable to track individual fish through time to determine growth more

accurately; however, this was not possible as excessive handling causes stress and

mortality. Quantitative relationships between mass and δ15N and δ13C were

sought, however no meaningful relationships were found. It is apparent that fish

did not grow sufficiently over six weeks to more than double their size and dilute

the isotopic signature of their previous diet with their new diet.

Diet significantly altered δ15N values of fish tissue over time largely

because the two experimental feeds differed in δ15N values. Fish fed the vegetable

feed had lower δ15N values than fish fed the fish-meal feed and this was due to the

vegetable feed having a much lower δ15N value (1.4 ‰) than the fish-meal feed

(8.8 ‰) and the hatchery feed (9.6 ‰), which were similar. The rates of change of

δ15N for fish fed the two experimental feeds were different and this was again due

to the vegetable feed having a much lower δ15N. All fish were reared on the same

diet before the experiment started and would have had similar δ15N values to

begin with. However fish fed the vegetable feed had a greater change in δ15N of

their diet, causing the δ15N in fish muscle to change more dramatically as fish

muscle isotopes approached a steady state at a lower δ15N. This is contrary to our

initial predictions that suggested isotope turnover would be faster in fish fed the

fish-meal feed, as it should more closely match the dietary requirements of

A. butcheri. However, the isotope concentrations in the two feeds were different

and appear to have had more of an effect on the rates of change than the dietary

composition or protein content over the short time period monitored.

61

A significant interaction among day, diet and temperature treatments was

detected for δ13C values. Tissue δ13C values initially increased or stayed the same

for the first 14 days, they then generally decreased over the last 28 days of the

experiment. The initial increase in δ13C may have been due to metabolism of

internally stored lipids. Lipids are known to be depleted in 13C and therefore have

more negative δ13C values compared to proteins and carbohydrates (DeNiro and

Epstein, 1977; Post et al., 2007). The two experimental feeds both had much

lower fat content compared to the hatchery feed, and the initial change to lower-

fat feeds may have stimulated fish to metabolise stored lipids, thus increasing in

δ13C as 13C-depleted lipids are metabolised. After 14 days the decrease in δ13C

may be explained by fish beginning to store lipids derived from their new diets.

These observations and hypotheses are supported by C:N ratio data, with C:N

values initially decreasing and then increasing after 14 days (unpublished data).

Fish reared at 23°C generally grew more than fish reared at 16°C, therefore it

would be expected that their δ13C values at 23°C would be more negative if they

put on more fat than fish reared at 16°C. However, this trend was only observed

for fish fed the vegetable feed. The fish fed the fish-meal feed had lower δ13C

values at 16°C than at 23°C after 42 days. The vegetable feed had slightly lower

δ13C values (-22.2 ‰) than the fish-meal feed (-21.1 ‰) and fish δ13C values at

23°C reflected this. However, the trend was reversed for the 16°C treatment. The

carbohydrate composition of the hatchery and vegetable feeds is not known and

this may affect the incorporation, or relative assimilation efficiency of carbon

compounds into fish muscle and therefore the δ13C (Kelly and Martínez del Rio,

2010).

62

Discrimination factors of δ15N were greater for fish reared at 16°C than

23°C and it appeared that fish reared at 16°C were in an isotopic steady state.

However if fish were not growing at 16°C, or were growing slowly, their tissue

would have retained more isotopic signature from the previous diet than fish

reared at 23°C. Therefore we cannot presume to quantify a discrimination factor

for δ15N for either feed at 16°C because of the historic isotopic signature. Fish

reared at 23°C had a smaller δ15N discrimination factor than those reared at 16°C

after 42 days, as would be expected due to increased metabolism and growth and

decreased fractionation through kinetic effects on chemical reactions. This agrees

with previous research, which has shown temperature effects on δ15N

discrimination for European sea bass muscle (Barnes et al., 2007), with a greater

discrimination at 11°C (4.41 ‰) than at 16°C (3.78 ‰). However some caution

should be taken in making numerical conclusions regarding the discrimination

factor for δ15N at 23°C in this experiment because δ15N values have not reached a

steady state.

Diet composition affected δ15N discrimination by fish muscle tissue, as

fish fed the fish-meal feed at 23°C changed in δ15N (1.4 ‰ from day 0 to day 42)

more than the difference between the two feeds (0.8 ‰) and this is beyond

precision and error rates. The fish-meal feed and the hatchery feed were quite

similar in their protein proximal composition and amino acid δ15N, and fish

maintained on the hatchery feed did not change in δ15N over 29 days. If there was

no effect of diet composition on discrimination factors then the change expected

between fish at day 0 and fish fed the fish-meal feed at day 42 should only be

0.8 ‰. This study shows that there are affects of diet on δ15N discrimination and

similar results have been found previously (Robbins et al., 2005; Tsahar et al.,

63

2008; Robbins et al., 2010). There is evidence that as dietary protein quality

increases, with regards to how well the protein matches an animal‟s requirements,

the discrimination of δ15N decreases (Robbins et al., 2005). Therefore it may be

that the fish-meal feed matched the dietary requirements of A. butcheri better than

the hatchery feed, as the discrimination at 42 days was less than the discrimination

for the hatchery diet. This would also indicate that the vegetable feed was a poor

match for A. butcheri‟s dietary requirements, as the discrimination factors were

large. However part of these large discrimination factors may be due to historical

isotopic signatures. The δ15N discrimination values we found for fish fed the

hatchery (5.0 ‰) and fish-meal feeds (4.4-5.7 ‰) are greater than the average

3.4 ‰ used by field researchers (Post, 2002). However, our values for δ15N

discrimination for these diets are within the range of results for various organisms

(Adams and Sterner, 2000; Gaston and Suthers, 2004; Connolly et al., 2005a; Mill

et al., 2007). Therefore a grand mean of our estimates for the hatchery and fish-

meal feed δ15N discrimination for A. butcheri muscle (5.1 ± 0.7 (1SD) ‰) may be

more appropriate to use than 3.4 ‰ to encompass the potential variation in diet

quality and temperature that cannot be quantified a-priori for food web or dietary

studies. However, we acknowledge that this may be high due to historic feed

effects and limited growth, particularly at 16°C.

Discrimination factors of δ13C were greater than the assumed 1 ‰ for all

diets. Fish muscle discrimination factors of δ13C for the two experimental diets

were greater than 3 ‰ at 42 days and discrimination for the hatchery diet was

2.7 ‰. Ratios of C:N were low (3.52), therefore lipid extraction or

mathematically de-fatting δ13C values should not greatly affect results. Regardless

of whether fish in this experiment were in an isotopic steady state or not, these

64

values are similar to discrimination factors that have been found by others. Gaston

and Suthers (2004) experimentally derived a discrimination value of 3.7 ‰ for

muscle δ13C of the marine fish Australian mado and Barnes et al. (2007) derived a

value of 3.13 ‰ for European sea bass muscle. Carbon sources in field situations

may be separated by 1 to 5 ‰; therefore source identification in food web studies

may be effected if no discrimination factor is applied. We recommend that a

discrimination factor for δ13C of 3.5 ± 0.7 ‰ (grand mean of all results ± SD) for

A. butcheri muscle be used in food web studies to encompass variation in diet and

temperature conditions.

Compound-specific amino acid δ15N

There was relatively large variation in δ15N of amino acids among fish within

treatment groups. Some amino acids (leucine, aspartic acid, and glutamic acid)

appear to have responded to diet and temperature treatments in a similar way to

the bulk δ15N. However there was much larger variation within treatment groups

for amino acid δ15N than for the bulk δ15N, obscuring our ability to detect

significant treatment effects. The accuracy and precision for bulk δ15N is much

better than compound-specific δ15N analyses, enabling us to detect significant

differences that are very small (0.7 ‰ was the average difference in bulk δ15N

between diet treatments across temperatures at 42 days). However, the precision

of our compound-specific δ15N analyses (0.9 ‰) is larger than the differences we

detected among treatment groups (minimum of 0.7 ‰) using bulk δ15N. Therefore

we cannot expect to detect such small differences using compound-specific δ15N

analysis. This indicates that if we are to use statistical tests on compound-specific

δ15N of amino acids the effect size needs to be very large to detect a difference,

much larger than precision.

65

Although change over time in δ15N of amino acids was only statistically

significant for glycine, there were several amino acids which showed decreasing

tends in δ15N over time: alanine, glycine, valine, leucine, isoleucine, proline,

aspartic acid, and glutamic acid. Most of the amino acids that decreased in δ15N

over time were „trophic amino acids‟ (Popp et al., 2007), with glycine the only

one classified in the „source amino acid‟ group. Although on average there were

temporal changes in δ15N of some amino acids as large a change as the temporal

change in bulk tissue δ15N (i.e. leucine, aspartic acid, and glutamic acid), there

was too much variation to detect significant differences. We expected that δ15N of

essential amino acids would take longer to respond to a diet switch as the amino

acids already incorporated into cells would have the δ15N of previous diets and

could only be renewed by replacing them with amino acids from the diet through

tissue renewal. Turnover of non-essential amino acids was expected to be faster as

animals can continuously manufacture non-essential amino acids as well as

incorporate them directly from their diet. Leucine is considered to be an essential

amino acid for fish (National Research Council (U.S.) and Committee on Animal

Nutrition, 1993) while glutamic acid and aspartic acid are considered non-

essential, yet all three changed by more than the average bulk tissue δ15N change

suggesting they are non-essential amino acids. It could be that we are in fact

identifying essential and non-essential amino acids through compound-specific

isotope analyses (Martínez del Rio et al., 2009 and references therein) and that

leucine is actually a non-essential amino acid for A. butcheri.

An effect of diet on δ15N of amino acids was only detected for glutamic

acid, although similar effects occurred for leucine and aspartic acid. Aspartic acid,

glutamic acid, and leucine changed the most in δ15N of all the amino acids over

66

time and would therefore be more likely to show treatment effects at 42 days.

Other amino acid δ15N values may have been still changing, particularly as the

bulk δ15N of muscle tissue was still changing for fish reared at 23°C. If the

experiment had continued we may have found more treatment effects across other

amino acids that were not changing as fast as leucine, aspartic acid, and glutamic

acid. Although we did not detect a significant effect of temperature on δ15N of

amino acids, it appears that temperature may have an effect as some amino acid

δ15N mirrored that of bulk tissue δ15N, for which there was an affect of

temperature. Therefore future research that includes seasonality within the

sampling regime and δ15N of amino acids may need to consider temperature, and

possibly growth effects.

The amino acids that were isotopically discriminated by fish, or those that

became more enriched than the bulk discrimination factor for each diet were

alanine, serine, leucine, isoleucine, proline, aspartic acid, and glutamic acid. The

amino acids that were isotopically discriminated less than the bulk discrimination

factor by fish were glycine, threonine, valine, phenylalanine, and lysine. These

patterns largely agree with those found by previous studies (McClelland and

Montoya, 2002; Popp et al., 2007). One exception to other researchers‟ findings is

that serine has previously been labelled a source amino acid, which is isotopically

discriminated less than the bulk discrimination factor for δ15N. Valine is another

exception, as it was labelled a trophic amino acid but here it groups out with the

source amino acids. It could be that serine is in fact a non-essential amino acid for

A. butcheri and that valine is essential for A. butcheri. The traditional

classification of essential and non-essential amino acids were derived from

experiments on mice and other researchers have found that these groupings do not

67

necessarily apply to other taxa (Zubay et al., 1995). However, whether compound-

specific isotope analyses can identify essential amino acids for animals requires

further testing.

Previous work on compound-specific isotope analysis of δ15N in amino

acids has focused on oceanic and near shore settings, to determine trophic position

of consumers in their food web from consumer samples alone (Popp et al., 2007;

Hannides et al., 2009; Olson et al., 2010). They have largely relied on McClelland

and Montoya‟s (2002) observation that δ15N of glutamic acid, or trophic amino

acids are enriched by 7 ‰ on average for each trophic level and have applied this

to estimate trophic position (see Hannides et al., 2009 for trophic position

equations). Our results showed that glutamic acid was enriched by 11.3 ±

1.0 (mean ± SE) ‰ for the hatchery feed, after approximately 100 days rearing,

which gives our best estimate of enrichment. This would put the experimental fish

in a 0.6 higher trophic position than they really are and this was found across all

amino acid trophic position equations used to date, such that trophic position of

A. butcheri was consistently over estimated by approx. 0.6.

The lack of significant effects of treatments on δ15N of some amino acids

may be due to large within group variation or it could be because there really are

no differences for particular amino acids. Many animals, particularly herbivores

and omnivores, have symbiotic relationships with gut microbes that break down

ingested food into soluble molecules, or manufacture essential amino acids that

are taken up by the host into their tissues (e.g. Torrallardona et al., 1996; Metges,

2000). It is possible that A. butcheri have gut microbes that digest their food,

particularly proteins, or manufacture particular amino acids such that they receive

an isotopically constant supply of particular amino acids regardless of what the

68

fish are actually ingesting. Our glutamic acid enrichment and over estimation of

trophic position support A. butcheri being supplied with amino acids by microbes.

There is also evidence that fish may have nitrogen conserving systems that

involve gut microbial activity (Singer, 2003), however Singer (2003) speculated

that this could only occur in ureotelic3 fish. Moeri et al. (2003) showed that the

δ15N of fish muscle and liver were not determined solely by diet, but that ambient

ammonia was taken in at the protein level in ammonotelic4 and ureotelic fish.

Therefore it is possible that the ammonia excreted by fish within tanks was being

re-absorbed into muscle at the protein level and potentially obscuring significant

differences in δ15N of amino acids. Fish were reared in tanks without flow-

through water, retaining excreted ammonia for up to two days at a time until a

portion of the water was changed. We speculate that results for δ15N of amino

acids may be different if this experiment had been carried out in flow-through

tanks. This would provide further evidence for ambient ammonia uptake into

proteins. Research into the gut microbes of A. butcheri and their digestive and

assimilative capacity would also further our understanding of why the δ15N of

some amino acids did not vary significantly.

This experiment and analyses of compound-specific amino acid δ15N has

shown that precision is a limiting factor in being able to detect significant

differences among groups. Therefore effect sizes will need to be large if statistical

analyses are to be applied successfully to such data. The results for individual

amino acids largely concur with previous groupings of source and trophic amino

acids. However, several differences occurred that have further raised the question

of using compound-specific δ15N of amino acids to determine essential and non- 3 Ureotelic organisms excrete excess nitrogen as urea. 4 Ammonotelic organisms produce soluble ammonia as a result of deamination. Many fish are ammoniotelic.

69

essential amino acids for different species. As few significant differences were

found in this experiment we echo the call for further experiments or analysis of

past experimental samples to clarify effects on amino acid δ15N and to validate

how applicable trophic position equations are across a range of species.

Conclusions

Our results highlight the need to experimentally derive isotopic discrimination

values for individual species, particularly at appropriate temperatures and on a

variety of diets as treatment effects were found. Temperature effects indicated that

fish muscle isotopic signatures reflect diets consumed during warmer growth

periods. Therefore isotope food web studies involving fish muscle should consider

sampling during summer, particularly towards the end of summer after fish have

grown and incorporated dietary isotopes. Compound-specific δ15N of amino acids

partially explained the treatment effects found on bulk tissue δ15N, with several

amino acids mirroring bulk tissue δ15N results. This indicates that δ15N of some

amino acids is also affected by temperature and diet, but whether this affects the

outcomes of trophic position calculations needs further investigation. Further

investigation into whether δ15N of amino acids can be used to determine essential

and non-essential amino acids is also required. Without sound experimental

validations of factors influencing isotope ratios, field applications may provide

misrepresentations of trophic relationships.

Acknowledgements

This research was funded by a Sir Mark Mitchell Foundation research grant to

T. Elsdon and B. Walther, and an ARC Linkage grant to B. Gillanders and

T. Elsdon. The Nature Foundation of South Australia provided funds for bulk

70

stable isotope analyses and the University of Adelaide supported A. Bloomfield to

travel to Hawaii for the compound-specific isotope analyses with E. Gier.

B. Walther was supported by an American Australian Association postdoctoral

fellowship and T. Elsdon was supported by an ARC postdoctoral fellowship

(APD) through an ARC Discovery grant. The authors would like to acknowledge

Nenah Mackenzie for bulk isotope analyses. Brian Popp and Karen Arthur

provided constructive comments on the manuscript. The experiment was done in

accordance with animal ethics guidelines of the University of Adelaide, under

permit S-074-2007.

71

Chapter Three: The influence of

temperature and elemental

concentration of diet on carbon and

nitrogen stable isotopes in fish

muscle, with a test of the

concentration dependent mixing

model

Experimental fish samples: Aldrichetta forsteri.

72

Chapter 3 Preamble

This chapter is a co-authored paper currently under peer-review with the Journal

of Fish Biology, with Bronwyn Gillanders and Travis Elsdon as co-authors. As

such it is written in plural.

In this chapter Travis Elsdon, Bronwyn Gillanders and I developed the

experimental design and supplied the funding. I did the experiment, caring for the

fish, with some help from other students (see acknowledgements). I prepared the

samples for analyses. I did all of the statistical analyses and wrote the manuscript

with input from co-authors.






73

The influence of temperature and elemental

concentration of diet on carbon and nitrogen stable

isotopes in fish muscle, with a test of the concentration

dependent mixing model

Abstract

The effects of temperature and elemental concentration of diet on δ13C and δ15N

incorporation rates and discrimination factors were investigated for yellow-eye

mullet Aldrichetta forsteri. Fish were reared at 16°C and 24°C and fed diets of

varying elemental concentration over 85 days. Fish reared at 24°C generally had

faster isotope incorporation, with the best estimate of half-life of δ15N being 27.2

days at 24°C. Fish reared at 24°C also had smaller δ15N discrimination, reflecting

increased growth and lower fractionation during chemical reactions. Values of

fish muscle δ13C reflected nutritional status of fish to a certain degree, with those

in good condition and high C:N ratios (and therefore higher lipid content) having

lower δ13C. No linear relationship was found between elemental concentration of

diet and isotope discrimination factors. Diet treatments of varying elemental

concentration showed potentially complex interactions of protein sparing,

nitrogen recycling and changes in metabolism of carbohydrates or proteins for

energy. Evidence of little change in δ15N during poor nourishment was also found.

When elemental concentration was used in mixing models, predictions of stable

isotope values were improved and were closer to measured isotopic values than

when elemental concentration was omitted. This work highlights the importance

74

of using elemental concentration in mixing models and supports the concentration

dependent mixing model.

Introduction

Stable isotopes of carbon and nitrogen are frequently used in ecological research

and are powerful tools for deciphering diets and food web dynamics, documenting

aquatic larval settlement, and detecting human impacts (e.g. Herzka et al., 2001;

Gaston et al., 2004; Connolly et al., 2005b). Stable isotope ratios of carbon (13C to

12C; δ13C) are known to vary among plants and algae with biological and chemical

processes (e.g. Peterson and Fry, 1987; Boon and Bunn, 1994; Smit, 2001). The

differences in δ13C among plants and algae enable researchers to determine diets

and track movements of fish (e.g. Herzka et al., 2002; Melville and Connolly,

2003; Hadwen and Arthington, 2007). Stable isotope ratios of nitrogen (15N to

14N; δ15N) increase with trophic level (Minagawa and Wada, 1984) and show

traces of human impacts through higher δ15N across food webs (Heaton, 1986;

Gaston and Suthers, 2004; Hadwen and Arthington, 2007).

Ecological studies often rely on predictable differences in isotopic ratios

between a consumer and its diet, i.e. discrimination factor. Discrimination of

stable isotopes occurs as a result of chemical and biological processes that alter

isotope ratios between sources (diet) and sinks (animal tissue), and are the total of

many fractionating processes (see Martínez del Rio et al., 2009 for more

discussion). Errors in discrimination factors used in ecological studies can

translate to erroneous contributions of dietary items, or misplacing organisms in

food webs (Post, 2002; Caut et al., 2009). Researchers use an average

discrimination factor of 3.4 ‰ for δ15N and 0-1 ‰ for δ13C per trophic level, as

recommended by Post (2002), when more specific values are unavailable.

75

However, the discrimination factors presented by Post (2002) are averages across

species yet discrimination factors vary among species and within species (DeNiro

and Epstein, 1978, 1981) leading to calls for researchers to experimentally

quantify discrimination factors for study species whenever possible (Martínez del

Rio et al., 2009). Simply quantifying discrimination factors will lead to specific

values for experimental conditions and these may not be applicable to wild

animals that experience a variety of environmental conditions. Therefore, when

deriving discrimination factors researchers should aim to encompass

environmental variability that will affect discrimination factors and to quantify the

variation.

Isotopic signatures of animal tissue do not instantaneously reflect diet.

Isotopes are gradually incorporated into animal tissue through growth and

metabolic activity (Fry and Arnold, 1982; Hesslein et al., 1993; Carleton and

Martínez del Rio, 2010), so the isotopic signature of animal tissue actually reflects

the animal‟s time-integrated diet. Muscle tissue is one of the most common tissue

types sampled for isotopic studies (Caut et al., 2009). Isotope incorporation rates

of muscle are thought to be dominated by growth, with little effect of metabolism

on isotopic change, particularly for fish (Chapter 2, Perga and Gerdeaux, 2005;

Zuanon et al., 2006; Bloomfield et al., 2011). However, growth of fish is also

affected by environmental factors including temperature, which in turn affects

isotope incorporation rates (Bosley et al., 2002; Witting et al., 2004; Barnes et al.,

2007). Discrimination factors are affected by temperature too, through kinetic

effects on chemical reactions, with warmer temperatures leading to smaller

discrimination factors and cooler temperatures resulting in larger discrimination

factors (Chapter 2, Bosley et al., 2002; Barnes et al., 2007; Bloomfield et al.,

76

2011). Temperatures of water bodies that fish inhabit vary seasonally and spatially

(e.g. Jones et al., 1996; Elsdon et al., 2009). Therefore, researchers need to

quantify the variability in discrimination factors and isotope incorporation rates

caused by temperature to improve the accuracy of ecological field studies using

stable isotopes.

Elemental concentration, or the percentage of atoms, in sources greatly

affects the results of isotope mixing models (Phillips and Koch, 2002). Isotope

mixing models are widely used to determine proportional source (diet)

contributions to a target organism (consumer) for dietary and ecological research

(e.g. Melville and Connolly, 2003; McClellan et al., 2010; Rush et al., 2010). In

the mixing model by Phillips and Koch (2002) the contribution of each source

was assumed to be proportional to the contributed mass multiplied by the

elemental concentration of the source. Phillips and Koch (2002) tested their model

by using isotope data from other published studies and extrapolated carbon and

nitrogen elemental concentrations of diet items in those studies from other

sources. Validation of the concentration dependent mixing model has received

little attention, with few experimental tests (Caut et al., 2008). Although more

complicated mixing models now exist, that provide the option of including

elemental concentration (e.g. Parnell et al., 2010), not all studies use elemental

concentration in analyses (e.g. Rush et al., 2010). Using elemental concentration

in mixing models has been supported by other published studies (Pearson et al.,

2003; Mirón et al., 2006). Elemental concentration can affect isotope

discrimination and incorporation rates (Pearson et al., 2003; Mirón et al., 2006).

There is, however, contradictory evidence as to whether increasing elemental

concentration causes an increase (Pearson et al., 2003) or decrease (Mirón et al.,

77

2006) in isotope discrimination, with some evidence suggesting that isotope

incorporation is also faster (Mirón et al., 2006). These studies have been done on

birds (Pearson et al., 2003) and bats (Mirón et al., 2006), with no published

research focused on elemental concentration in fish diets. Therefore further

research is warranted into the effects of elemental concentration on the

discrimination factors and isotope incorporation rates of fish.

Omnivores are excellent candidates for isotopic investigations as they tend

to eat a range of dietary components that are readily available to them (e.g. Webb,

1973; Sarre et al., 2000; Chuwen et al., 2007; Hadwen et al., 2007). Dietary

components of omnivores can vary greatly in their elemental concentration

(Pearson et al., 2003), making research into effects of elemental concentration on

isotopes in omnivores pertinent. Yellow-eye mullet Aldrichetta forsteri

(Valenciennes, 1836) are a common, omnivorous, euryhaline fish that can be

found in bays, estuaries, and open coastlines around New Zealand, the Chatham

Islands, and Australia: from Newcastle, NSW, along the south coast to Shark Bay,

WA, including around Tasmania (Kailola et al., 1993; Armitage et al., 1994). The

abundance and large geographical distribution of A. forsteri make them ideal fish

for ecological investigations. Previous dietary studies, or stomach content

analyses, of A. forsteri have been hampered by the high frequency of empty

stomachs and high proportions of unidentifiable matter and detritus in the gut

(Webb, 1973; Platell et al., 2006). Isotope studies have the potential to generate

dietary data more efficiently over large areas than traditional stomach content

analyses.

The effects of temperature and dietary elemental concentration on δ13C

and δ15N incorporation rates and discrimination factors of A. forsteri muscle were

78

experimentally tested. Fish were reared at two temperatures and fed two diets that

differed in their elemental concentrations. It was predicted that fish reared at

warmer temperatures would have smaller discrimination factors and faster

incorporation rates than fish reared at cooler temperatures (Barnes et al., 2007). It

was also expected that fish fed a diet with high nitrogen and carbon concentration

would have faster isotope incorporation rates and larger isotope discrimination

factors than fish fed a diet with lower nitrogen and carbon concentration (Mirón et

al., 2006). Experimental feeds were mixed together in varying proportions and fed

to fish to test if there was a linear relationship between dietary elemental

concentration and isotopic signatures of fish (Adams and Sterner, 2000). To test

the importance of elemental concentration in mixing models predictions of mixing

models were compared, with and without elemental concentration, for δ13C and

δ15N of fish fed mixed diets with measured isotopic values (Caut et al., 2008).

Methods

Treatments

There were two components of this study: one focusing on isotope incorporation

and discrimination factors; and the other focusing on elemental concentration of

diet in mixing models. For the isotope incorporation and discrimination factors

component fish were reared under orthogonal treatments of two temperatures

(16°C or 24°C), two diets (100 % chicken or 100 % Artemia; encompassing

different C and N concentrations), and seven rearing times (2, 7, 14, 28, 42, 60,

and 85 days after diet switch) with two replicate tanks per treatment (56 tanks in

total) and ten fish per tank. Entire tanks were sacrificed on days mentioned above

so that fish density was not varied. Temperatures were chosen to reflect local

79

summer and winter temperatures of environments where A. forsteri have been

caught in the past (Jones et al., 1996). We acknowledge that daily water

temperatures can vary by as much as 10°C in shallow estuaries, where A. forsteri

are found, and that oscillating temperatures may influence incorporation rates and

discrimination factors in intricate ways. However, the effect of temperature on

these parameters needs to be established first before investigating how oscillating

temperature affects isotope incorporation and discrimination.

To further investigate effects of elemental concentration in diets on

isotopes in fish and discrimination factors, fish were fed mixtures of chicken and

Artemia (see diet details below) for 85 days at 24°C. There were also two

replicate tanks for each mixed diet treatment, with ten fish per tank. For the

component focusing on using elemental concentration in mixing models, data

from the above experiment were used to test how accurate mixing models were at

predicting isotope ratios with and without elemental concentration in their

calculations.

Diet details

Chickens, used as fish feed, had been feeding on a diet of approximately 20-30 %

corn and were sourced from a commercial chicken farm. For initial diet

preparations two whole chickens were boned and fat removed, with muscle being

pureed in a food blender. The puree was then frozen into small blocks. Frozen

Artemia blocks were sourced from a commercial Artemia farm. To prepare the

mixed diets a sample of pureed chicken and frozen Artemia were mixed together

in ratios of 25:75, 50:50, and 75:25 measured by wet weight, in a food blender.

Hereafter mixes are referred to with the percentage of chicken first such that

25:75 refers to a mixture of 25 % chicken and 75 % Artemia. The mixtures were

80

frozen into small blocks weighing approximately 3-4 g, which were similar in

mass to chicken and Artemia blocks.

Diets ranged in carbon concentration from 33 % for Artemia to 52 % for

chicken and nitrogen concentration ranged from 7 % for Artemia to 14 % for

chicken (see Table 3.1). The elemental concentration of chicken is typical of

animal matter (muscle) with carbon usually being 45-50 % and nitrogen usually

being 14 % (Bloomfield, unpublished data). The nitrogen concentration of

Artemia was much lower than that of chicken, however, it is not as low as that

usually found in plant matter, which can be as low as 1-3 % (Bloomfield

unpublished data, Mill et al., 2007). The carbon concentration in plant matter can

be highly variable (40 % ± 7 (1SD); Bloomfield unpublished data) but is often

lower than that of animal muscle. Therefore Artemia represents dietary items with

a lower carbon and nitrogen concentration (%) that A. forsteri may encounter in

the wild, such as plant-derived matter. However it is acknowledged that the

nitrogen concentration of Artemia is much higher than typical plant matter. Fish

were fed Artemia over a pure algal based diet as the amount of algal feed required

to sustain fish would be large, leading to substantially different feed rates, and

such volumes would be difficult to produce. Even though fish were fed chicken

and Artemia at different rates, this difference is much less than it would have been

if fish had been fed a pure algal based feed.

81

Table 3.1 Carbon (C) and nitrogen (N) elemental concentration (% mean ± SE) of

feeds (Artemia, mixed feeds: Chicken:Artemia mixed in mass proportions, 25:75,

50:50, and 75:25, Chicken and Worms) used in the experiment.

Feed C (%) N (%)

Artemia 32.9 ± 1.8 6.6 ± 0.4

25:75 45.0 ± 0.5 11.7 ± 0.1

50:50 47.0 ± 0.7 12.6 ± 0.1

75:25 48.3 ± 0.4 13.6 ± 0.1

Chicken 51.8 ± 0.9 13.8 ± 0.4

Worms 53.5 ± 0.3 10.1 ± 0.1

Fish rearing

Juvenile (average standard length ± SE of 41 ± 1.5 mm, range = 32 – 51 mm;

average mass ± SE of 1.02 ± 0.13 g, range = 0.14 – 1.97 g) A. forsteri were

collected from Gawler River, South Australia, in October 2008 using a seine net.

Fish were placed in 50 L containers with aeration and transported to the

University of Adelaide aquarium room for acclimation to experimental conditions.

Fifteen fish were sacrificed on the day of collection to represent wild caught

A. forsteri for size (length and weight) measurements and stable isotope analyses.

All fish were sacrificed using an ice water slurry. Fish were housed in two 800 L

tanks at an ambient temperature of 17°C and a 12 hr day/night cycle. During

acclimation fish were fed live black worms (Lumbriculus sp.), or occasionally

frozen worms of the same species, two to three times daily for two months. Fish

were observed to eat black worms vigorously and competition for food within

tanks was high. A group of 20 fish was fed black worms for 116 days total to

82

allow them to equilibrate with worms as their diet for stable isotope analyses.

Water temperature was increased to 20°C over two months, due to increased

ambient summer temperatures. Water quality was maintained by daily water

changes of approximately 30-50 % during this acclimation period.

After two months fish were randomly allocated to 40 L experimental tanks

at a density of ten fish per tank. In the 40 L tanks fish were acclimated to either

16°C or 24°C over several days. Fish were fed black worms during the

temperature acclimation period and fish were observed to eat them. On the first

day of the experiment fish were fed frozen blocks of chicken or Artemia, or a

mixture of the two, cut into smaller pieces. Initially fish were fed a quarter of a

frozen food block (approximately 1 g) per 40 L tank of 10 fish (equating to

approximately 5 % body mass per feed) for each feed (2-3 feeds per day). Not all

fish were observed to eat the frozen chicken initially, however, fish fed on

chicken after a couple of days. Fish ate most of the frozen Artemia and mixed

diets. Feeding rates were increased during the experiment to coincide with

increased growth. If any food was left in tanks after 10 mins it was removed to

maintain water quality. Water quality in 40 L tanks was further maintained by

quarter water changes every two days.

Sample preparation and analyses

Sacrificed fish were frozen whole to -20°C and later defrosted for dissection of

dorsal muscle tissue. When fish were defrosted they were individually weighed

(mass (g)) and measured (standard and total lengths (mm)), with the five largest

fish in each tank having dorsal muscle samples taken. The five largest fish were

chosen as it was thought that they would be more likely to have consumed more

feed and therefore have incorporated more isotopes from their diet into their tissue

83

than the smaller fish. Random samples of pureed chicken, Artemia and mixed

diets were prepared for elemental concentration and stable isotope analyses. Black

worms were also randomly sampled over the duration of the acclimation period.

Samples of fish dorsal muscle and diets were freeze-dried and ground into a

powder using an agate mortar and pestle. Fish muscle samples did not have lipids

extracted as most samples had C:N below 3.5, the cut off for desired lipid

extraction in fish (Post et al., 2007). Also, samples were compared within a

species and lipid extraction is known to introduce more variation into δ15N of

samples (Elsdon et al., 2010). Lipids were not extracted from feeds, as fish

consume the whole feed and may metabolise lipids. Samples of fish muscle and

diets were weighed into tin capsules for elemental concentration and stable

isotope analyses of carbon and nitrogen. Elemental concentration and stable

isotope analyses were done on a GV Isoprime (Manchester UK) continuous flow

isotope ratio mass spectrometer coupled to a Eurovector (Milan Italy) elemental

analyser 3000 at Griffith University, Queensland, Australia. International and

internal standards (N: Ambient Air, IAEA-305a, C: ANU Sucrose, Acetanilide,

Working standards: 'Prawn') were run in parallel with fish and diet samples to

calibrate machine results. Average precision of the elemental analyser was 0.61 %

for carbon and 0.29 % for nitrogen (1 SD), with average accuracy of 0.18 % for

carbon and 0.05 % for nitrogen (average deviation from theoretical value).

Average precision of the mass spectrometer was 0.06 ‰ for δ13C and 0.29 ‰ for

δ15N (1 SD), with average accuracy of 0.02 ‰ for δ13C and 0.04 ‰ for δ15N

(average deviation of results from known value).

84

Statistical analyses

Isotopes of experimental feeds were analysed using a one-way PERMANOVA

(Euclidean distance used for resemblance matrix, unrestricted permutations of raw

data, 999 unique permutations, Anderson, 2001) to see if there was a difference in

δ13C and δ15N compositions of feeds (excluding worms which were fed to fish

prior to treatments). Fish mass, standard length, and Fulton‟s K of all fish reared

under experimental conditions were analysed by a four-factor ANOVA to see if

experimental treatments affected fish growth and condition. Four-factor ANOVAs

consisted of treatments of day (excluding day 0), diet (chicken and Artemia-fed

fish), and temperature (all factors were treated as fixed factors) with tank as a

random nested factor within day × diet × temperature. Fish muscle δ13C, δ15N, and

C:N were analysed in a similar manner in separate ANOVAs. C:N ratios can be

an indicator of how „fat‟ or lipid-rich an animal is, with increasing C:N ratios

indicating more lipids in tissues (Post et al., 2007) and potentially better animal

condition (Kaufman and Johnston, 2007).

Effects of mixing diets on δ13C and δ15N of fish muscle were tested in

separate, two-factor ANOVAs. Factors in the ANOVAs were diet (fixed) and tank

as a nested random factor. The ANOVAs were performed on fish tissue δ13C and

δ15N from fish reared for 85 days at 24°C and fed either chicken or Artemia or

mixtures of the two (0:100, 25:75; 50:50, 75:25 and 100:0). Diet effects on length,

mass, Fulton‟s K, and C:N ratios of fish muscle were also tested in similar two-

factor ANOVAs. Relationships between isotopes of fish muscle and elemental

concentration in feed were investigated using regression analyses (dynamic

fitting) in Sigma Plot 11.0.

85

Discrimination factors for carbon and nitrogen were calculated for fish

reared for the longest period of time on their respective diets (85 days for all diets

except worm-fed fish which were reared for 116 days). The average of diet

isotopes was subtracted from individual fish tissue δ13C and δ15N values.

Treatment effects on δ13C and δ15N discrimination were tested using separate

ANOVAs. Two ANOVA designs were done on discrimination factors, as

temperature was not replicated completely across all experimental diets. One

ANOVA design had tank as a nested random factor within diet treatments (fixed)

of chicken or Artemia or mixtures of the two for fish reared at 24°C. Interactive

effects of diet and temperature were tested in three-factor ANOVAs, with tank as

a nested random factor within diet and temperature (both fixed factors) for fish

fed only chicken or Artemia (100 %) and reared at 16°C or 24°C.

Mixing models

Effects of carbon and nitrogen elemental concentration on outcomes of isotope

mixing models were investigated by calculating the expected isotopic signatures

of fish muscle tissue (with and without elemental concentration) and comparing

them with measured isotopic signatures. To calculate the expected isotopic

signatures it was assumed that all food that was consumed was assimilated in

relative proportions. The mass balance model (Phillips and Gregg, 2001) was used

to calculate expected isotopic signatures for carbon and nitrogen in fish muscle

tissue without elemental concentration:

δ13CM = ƒCh(δ13CCh + Δ13Ctissue-Ch) + ƒA(δ13CA + Δ13Ctissue-A) (1)

δ15NM = ƒCh(δ15NCh + Δ15Ntissue-Ch) + ƒA(δ15NA + Δ15Ntissue-A) (2)

1 = ƒCh +ƒA (3)

86

where the subscript M represents the fish tissue from fish fed the mixed diet under

question; subscript Ch represents the pureed chicken muscle; subscript A

represents the Artemia blocks; ƒ represents the fractional contribution of chicken

or Artemia to the mixed diet under question, by mass; and Δ13Ctissue-diet is the

isotopic discrimination of chicken or Artemia experimentally derived from fish

reared for 85 days at 24°C. Note that although fish may not have all reached

isotopic equilibrium after 85 days (see Results), as fish had been feeding on mixed

diets for the same period of time isotope integration and discrimination would be

proportionally similar. Therefore the un-equilibrated discrimination factors used

are valid for the mixing models as fish had been reared on diets for the same

period of time.

The concentration dependent equations of Phillips and Koch (2002) were

also used, but they were reduced to two sources. Let ƒCh,B and ƒA,B represent the

fractions of consumed biomass (B) of chicken (Ch) and Artemia (A) respectively

(i.e. the proportion of chicken or Artemia in the mixed diet); and ƒCh,C, ƒA,C, ƒCh,N

and ƒA,N represent the fractions of consumed carbon (C subscript) or nitrogen (N

subscript) from chicken or Artemia. The mass balance equations can then be

written as:

δ13CM = ƒCh,C(δ13CCh + Δ13Ctissue-Ch) + ƒA,C(δ13CA + Δ13Ctissue-A) (4)

δ15NM = ƒCh,N(δ15NCh + Δ15Ntissue-Ch) + ƒA,N(δ15NA + Δ15Ntissue-A) (5)

The diet fractional contributions for C, N, and biomass are constrained to sum to

1, as per Phillips and Koch (2002):

1 = ƒCh,C +ƒA,C (6)

1 = ƒCh,N +ƒA,N (7)

1 = ƒCh,B +ƒA,B (8)

87

Phillips and Koch‟s (2002) concentration dependent mixing model assumes that

the contribution of each food or source (chicken or Artemia) to the consumer‟s

tissue (fish muscle) isotopic signature is proportional to the elemental

concentration in the food multiplied by the assimilated biomass. However, here it

was assumed that it was proportional to the consumed biomass to enable back-

calculations so that they can be compared with measured isotopic signatures. Let

(C)Ch, (C)A, (N)Ch and (N)A represent the carbon and nitrogen concentrations in

chicken and Artemia. Therefore, using Phillips and Koch‟s concentration

dependent model the following were derived and used:

(9)

(10)

(11)

(12)

in equations 4 and 5 to derive expected isotopic signatures, accounting for

elemental concentration, of fish muscle for fish fed mixed diets. These results

were then compared with those derived from strict mass balance calculations and

measured isotopic signatures to see if accounting for elemental concentration

improves predictions of δ13C and δ15N in fish muscle tissue.

Results

Diets

The elemental concentrations of mixed feeds did not vary linearly with

proportional mass contribution as expected (see Table 3.1). The elemental

concentrations, and consequently the stable isotope signatures, were skewed

88

toward the chicken puree (see Table 3.1, Fig. 3.1), however, elemental

concentration did vary co-linearly between % carbon and % nitrogen.

Figure 3.1 Feed δ15N and δ13C values (mean ± SE ‰) used in the experiment.

Note subscript letters denote diets that were not significantly different in isotopic

composition.

Isotope ratios of live black worms did not vary greatly over the duration of

the experiment (δ15N = 10.3 ± 0.1, δ13C = -22.29 ± 0.02, mean ± SE ‰) nor did

isotopes of chicken (δ15N = 4.0 ± 0.2, δ13C = -22.21 ± 0.19, mean ± SE ‰).

However, isotopes of Artemia were more variable (δ15N = 8.1 ± 0.3, δ13C = -18.68

± 0.57, mean ± SE ‰), which subsequently affected the mixed feeds, particularly

the 25:75 mix (δ15N = 5.5 ± 0.2, δ13C = -21.23 ± 0.33, mean ± SE ‰). There was

a significant difference in isotope composition (δ13C and δ15N) among all feeds

δ13C

-23 -22 -21 -20 -19 -18 -17

δ15 N

3

4

5

6

7

8

9

10

11

25:75b

50:50b

75:25a

Artemia

Chickena

Worms

89

(PERMANOVA F4,34 = 13.035, p = 0.001) except for 25:75 and 50:50, and 75:25

and chicken (see Fig. 3.1), which were similar in pair-wise analyses. The

similarities in isotope signatures of particular diets are likely due to more C and N

coming from chicken than Artemia.

Chicken and Artemia fed fish (100 %)

Fish growth

There was an interaction among day, diet, and temperature for fish mass and

standard length (Table 3.2). On average fish grew over the duration of the

experiment, but, most of this growth occurred after 28 days. Fish fed chicken were

generally larger (heavier and longer) than fish fed Artemia (Figs 3.2a and b). Fish

size (mass and length) was largely unaffected by temperature for the first 28 days,

after which fish reared at 24°C were more often larger than fish reared at 16°C,

particularly for fish fed chicken (Figs 3.2a and b).

90

Table 3.2 Four factor analysis of variance (ANOVA) of treatment effects on fish

mass, standard length (SL) and condition (Fulton‟s K) for all fish fed 100 % of

either chicken or Artemia and reared at 16°C or 24°C (excludes day 0). Bolded

numbers indicate significant effects (p < 0.05). Data were not transformed.

Mass SL Fulton's K

Source of variation df MS p MS p MS p

Day 6 28.260 0.001 1154.400 0.001 0.899 0.001

Diet 1 70.730 0.001 1155.000 0.001 5.417 0.001

Temperature 1 5.201 0.047 293.120 0.044 0.093 0.180

Day x Diet 6 4.600 0.007 105.690 0.140 0.264 0.001

Day x Temperature 6 2.290 0.116 77.494 0.323 0.049 0.465

Diet x Temperature 1 9.104 0.010 43.350 0.407 0.932 0.001

Day x Diet x Temp. 6 4.494 0.008 190.940 0.019 0.175 0.006

Tank (Day x Diet x

Temp.) 28 1.187 0.359 60.306 0.503 0.051 0.001

Residual 476 1.087 63.652 0.012

Total 531

91

Figure 3.2 Average (± SE) (a) mass (g) (b) standard length (SL, mm) and (c)

Fulton‟s K for fish fed chicken (squares) or Artemia (circles) and reared at either

16°C (solid symbols) or 24°C (open symbols) pooled over tanks. Note: points are

offset slightly around sampling days.

0 20 40 60 80 100

Mas

s (g

)

0

1

2

3

4

5

0 20 40 60 80 100

SL (m

m)

40

45

50

55

60

65

Day

0 20 40 60 80 100

Fulto

n's

K

1.0

1.2

1.4

1.6

1.8

2.0

a)

b)

c)

92

Fish condition

There was an interaction among day, diet, and temperature in four-factor ANOVA

of Fulton‟s K (Table 3.2). Generally fish fed chicken and reared at 24°C were in

the best condition (highest Fulton‟s K), followed by chicken-fed fish reared at

16°C, then Artemia-fed fish reared at 16°C, with Artemia-fed fish reared at 24°C

in the worst condition (lowest Fulton‟s K) for the duration of the experiment (see

Fig. 3.2c). Over the first 28 days, condition of fish fed Artemia generally

decreased and condition of chicken-fed fish increased (Fig. 3.2c). After 28 days,

condition of fish fed Artemia increased and levelled off, and this was likely due to

increased feeding rates. Another increase in condition for Artemia-fed fish was

not detected after 65 days, when feed rates were again increased. Despite a

significant effect of tank of Fulton‟s K (Table 3.2), data are presented pooled

across tanks as there were significant treatment effects of diet and temperature

which interacted with time (Fig. 3.2c). Carbon to nitrogen (C:N) ratios of fish

muscle showed similar patterns to Fulton‟s K and were affected by diet, but this

depended on temperature and time (Table 3.3, Fig. 3.3a).

93

Table 3.3 Four factor ANOVA of treatment effects on δ13C, δ15N, and C:N ratios

of fish muscle (excluding day 0) for fish fed chicken or Artemia and reared at

either 16°C or 24°C over 85 days. Bolded numbers indicate significant effects

(p < 0.05). Data were not transformed.

δ13C δ15N C:N

Source of Variation df MS p MS p MS p

Day 6 0.985 0.013 31.279 0.001 0.217 0.001

Diet 1 46.738 0.001 354.82 0.001 5.342 0.001

Temperature 1 2.039 0.014 20.526 0.025 0.675 0.001

Day x Diet 6 5.249 0.001 5.645 0.154 0.137 0.001

Day x Temperature 6 0.368 0.289 4.482 0.292 0.028 0.102

Diet x Temperature 1 1.336 0.041 6.891 0.164 0.274 0.001

Day x Diet x Temp. 6 0.155 0.756 5.141 0.216 0.077 0.001

Tank (Day x Diet x

Temp.) 28 0.295 0.016 3.279 0.012 0.014 0.554

Residual 224 0.172 1.718 0.015

Total 279

94

Day

0 20 40 60 80 100-22.0

-21.5

-21.0

-20.5

-20.0

-19.5

-19.0

-18.5

0 20 40 60 80 1009

10

11

12

13

14

15

16

17

0 20 40 60 80 100

C:N

3.0

3.2

3.4

3.6

3.8

4.0

δ15 N

δ13 C

a)

b)

c)

95

Figure 3.3 Average (± SE) (a) C:N ratios, (b) δ15N (‰) and (c) δ13C (‰) of fish

muscle tissue over the duration of the experiment for fish fed chicken (squares) or

Artemia (circles) and reared at either 16°C (solid symbols) or 24°C (open

symbols) pooled over tanks. Isotope data for day 0 fish are taken from fish kept on

worms for 116 days total. Lines of best fit are shown (solid line – chicken-fed fish

reared at 24°C; dashed line – chicken-fed fish reared at 16°C; dotted line –

Artemia-fed fish reared at 24°C; long dashed line – Artemia-fed fish reared at

16°C). No lines are fitted to δ15N of Artemia treatments as the change over time

was inconsistent, in differing directions and there was large variation among

individuals. See Table 3.4 for regression details.

Table 3.4 Regression analyses details of δ15N and δ13C (y, ‰) over time (x, days)

in Figs 3.3b) and c) for fish fed chicken or Artemia at 16°C or 24°C over 85 days.

Isotope Diet Temperature (°C) Equation r2 p

δ15N Chicken 24 y = 9.49 + 4.86e(-0.026x) 0.99 <0.0001

16 y = -3.43 + 17.41e(-0.0019x) 0.83 0.0114

δ13C Chicken 24 y = -20.19 - 0.013x 0.89 0.0004

16 y = -20.13 - 0.016x 0.91 0.0003

Artemia 24 y = -20.10 + 0.012x 0.83 0.0017

16 y = -20.19 + 0.006x 0.44 0.0700

96

Isotope incorporation

There was a significant effect of day, diet and temperature on nitrogen isotopes of

fish muscle (Table 3.3). Generally fish reared at 24°C had lower δ15N than fish

reared at 16°C and fish fed chicken had lower δ15N than fish fed Artemia

(Fig. 3.3b). Fish fed chicken at 24°C displayed the classic exponential decay

relationship found in many other experiments. These fish had the fastest isotope

turnover as δ15N changed the most over the 85 days of the experiment and the

exponential co-efficient is larger than that for fish reared at 16°C and fed chicken

(Table 3.4). Fish fed chicken and reared at 24°C appear to be near equilibrium,

with regression analysis finding the asymptotic δ15N value to be 9.49 ± 0.33

(SE) ‰. The half life, or the length of time for half the change in δ15N to occur,

for δ15N of fish fed chicken at 24°C was 27.2 days. Fish fed chicken and reared at

16°C may be in equilibrium with their diet, as there was no significant difference

in δ15N of fish tissue sampled on days 60 and 85. However, there was a significant

difference between tanks on day 60 for this treatment. Therefore it is likely that

the similarity in δ15N between days 60 and 85, for fish fed chicken and reared at

16°C, is due to large variation pooled over tanks on day 60 and that fish δ15N was

still changing. The line of best fit for δ15N of fish fed chicken at 16°C was more

similar to a linear decrease than to an exponential decay curve, which suggests

fish would need to be reared longer to determine where an asymptote occurs. The

asymptote could be expected to be between 10 and 11 ‰, slightly higher than that

for fish reared at 24°C, due to larger discrimination at cooler temperatures (Bosley

et al., 2002; Barnes et al., 2007). Exponential decay curves were not fitted to δ15N

of Artemia-fed treatments as the change over time was not unidirectional for

either temperature (Fig. 3.3b).

97

There were significant interactions between diet and temperature, and

between day and diet on δ13C of fish muscle (Table 3.3). The diet and temperature

interaction reflected a difference between temperature treatments of fish muscle

δ13C from fish fed Artemia, but there was no difference between temperature

treatments for chicken-fed fish. Fish fed Artemia and reared at 24°C generally had

higher δ13C than fish reared at 16°C (Fig. 3.3c). The rate of change of δ13C,

measured by the slope of the line, was twice as fast for fish fed Artemia and

reared at 24°C than those reared at 16°C (see Table 3.4, Fig. 3.3c). The diet and

time interaction was due to fish fed Artemia increasing in δ13C over time, whereas

fish fed chicken decreased in δ13C. Fish muscle δ13C for all treatments did not

appear to approach an asymptote so no attempt was made to fit exponential decay

curves. These data suggest that fish muscle δ13C is continuing to change and that

fish may not have equilibrated with their diets. Lines of best fit were linear, with

r2 being greater than 0.8 for most regressions (Table 3.4), except for Artemia-fed

fish reared at 16°C (r2 = 0.44).

There was a significant effect of tank on δ15N and δ13C of fish muscle

(Table 3.3). For δ15N three pairs of tanks showed significant differences, whereas

for δ13C four pairs of tanks showed significant difference between them. Most of

the pairs of tanks that showed significant differences were not the same in δ13C

and δ15N analyses, except for one pair. Differences between pairs of tanks were

generally not biologically meaningful, as they were due to small variation within

data sets. Given main effects are the primary concern of this paper, individuals

from all tanks were used for analyses and all graphs show averages pooled for

tanks.

98

Mixed diets

There was a significant effect of diet on fish size (mass: F4,84 = 6.10, p < 0.05, and

length: F4,84 = 7.97, p < 0.05), Fulton‟s K (F4,84 = 12.95, p < 0.01) and C:N ratios

(F4,49 = 13.01, p = 0.01) of muscle from fish reared for 85 days at 24°C on mixed

diets (Table 3.5). The effects of diet on fish size were similar for mass and

standard length (see Table 3.5) with significant differences in fish size (length and

mass) between fish fed high percentages of Artemia and those fed low

percentages. Fish fed the 25:75 mix were the smallest in mass and length on

average, followed by Artemia-fed fish, fish fed 50:50 mixture, then chicken-fed

fish, and fish fed the 75:25 mix were the largest fish on average. The diet effect on

fish condition, or Fulton‟s K, was due to significant differences between Artemia-

fed fish and those fed all other diets except for 25:75; which were similar in

Fulton‟s K to Artemia-fed fish but also different from all other diets (see

Table 3.5). Artemia-fed fish had the lowest condition, based on Fulton‟s K, of all

treatment groups. The significant effect of diet on C:N ratios was due to

differences between fish fed 25:75 diets and all other diet groups, bar Artemia-fed

fish (100 %), which were also low in C:N and similar to fish fed the 25:75 diet

(see Table 3.5). Chicken-fed fish also had a significantly greater C:N ratio than

fish fed 50:50 diets.

99

Table 3.5 Average (± SE) of mass, standard length (SL), condition (Fulton‟s K),

and C:N ratios of muscle from fish fed chicken (C) or Artemia (A) or mixtures of

the two (C:A; 0:100, 25:75, 50:50, 75:25, 100:0) for fish reared at 24°C after 85

days. Note subscript letters denote diets with values that are not significantly

different.

Diet Mass (g) SL (mm) Fulton‟s K C:N

Artemia (100 %) 2.58 ± 0.23a,c 56 ± 2a,c 1.41 ± 0.01a 3.4 ± 0.0a,b,c

25:75 2.25 ± 0.20a 53 ± 2a 1.41 ± 0.02a 3.3 ± 0.0a

50:50 3.59 ± 0.39b,c 61 ± 2b,c 1.52 ± 0.02b 3.5 ± 0.0c

75:25 4.57 ± 0.46b 65 ± 2b 1.60 ± 0.03b 3.7 ± 0.1b,c

Chicken (100 %) 4.16 ± 0.38a,b 61 ± 2a,b 1.75 ± 0.04b 3.7 ± 0.1b

There was a significant effect of diet on δ13C (F4,49 = 45.33, p = 0.001) and

δ15N (F4,49 = 19.91, p= 0.01) of fish muscle for fish fed mixed diets (Fig. 3.4).

There were significant differences in δ13C and δ15N of fish tissue for those fed

chicken or Artemia (100 %), however, there were not always differences among

fish fed mixed diets. Fish fed mixtures with percentage contributions that were

next to each other on a proportional scale had similar isotope ratios and more

distant mixtures had significantly different isotope ratios (Fig. 3.4).

100

Figure 3.4 Fish muscle δ15N and δ13C values (mean ± SE ‰) after 85 days

rearing at 24°C for fish fed Artemia (A) or chicken (C) or mixtures of the two

(C:A; 0:100, 25:75, 50:50, 75:25, 100:0). Note subscript letters denote δ15N and

δ13C values that are not significantly different; and subscript numbers denote δ15N

values only that are not significantly different. Linear regression of δ15N against

δ13C: r2 = 0.88, p < 0.05.

δ13C

-23 -22 -21 -20 -19 -18

δ15 N

9

10

11

12

13

14

Artemia125:75a,1

50:50a,b

75:25bChickenb

101

There was a significant effect of tank on δ13C (F5,49 = 2.88, p < 0.05) and

Fulton‟s K (F5,84 = 3.00, p < 0.05) of fish reared for 85 days at 24°C and fed

chicken or Artemia or a mixture of the two. The pairs of tanks that were

significantly different in δ13C and Fulton‟s K were not the same. The pairs of

tanks that were significantly different in Fulton‟s K were the chicken-fed tanks

and the difference between tanks was smaller than the difference between other

pairs of tanks. The differences in δ13C of fish tissue and Fulton‟s K among tanks

were very small and variance in δ13C was mostly small within treatment groups

(see Fig. 3.4), therefore tanks are presented pooled.

Fish muscle δ15N decreased with increasing nitrogen in the diet (see

Fig. 3.5a), however, the regression is not statistically significant (F1,4 = 6.52, p >

0.05). The δ13C of fish muscle decreased linearly as the percentage of carbon in

the diet increased (F1,4 = 42.17, p < 0.01; Fig. 3.5b).

102

Figure 3.5 Fish muscle (a) δ15N and (b) δ13C (mean ± SE ‰) in relation to

elemental concentration ((N) or (C) mean ± SE %) of diet after 85 days rearing at

24°C.

(a)

δ15 N

fish

mus

cle

δ15Nmuscle = 16.44 – 0.43(%Ndiet) r2 = 0.685

%N in diet

4 6 8 10 12 14 169

10

11

12

13

14

15

(b)

δ13 C

fish

mus

cle

δ13Cmuscle = -14.86 – 0.12(%Cdiet) r2 = 0.934

%C in diet

30 35 40 45 50 55-22.0

-21.5

-21.0

-20.5

-20.0

-19.5

-19.0

-18.5

-18.0

103

Isotope discrimination

There was a significant effect of diet on both δ13C (F4,49 = 18.64, p < 0.01) and

δ15N (F4,49 = 5.82, p < 0.05) discrimination. For δ15N discrimination the only

significant difference was between Artemia and the 75:25 mixed diet (Fig. 3.6a).

Fish fed live worms had the smallest δ15N discrimination, followed by fish fed

100 % Artemia, then fish fed 75, 50 and 100 % chicken, with fish fed 25 %

chicken having the greatest δ15N discrimination of all treatments (see Fig. 3.6a).

Tissue δ13C discrimination was similar between fish fed 100 % chicken and fish

fed 25, 50 or 75 % chicken, however, discrimination was significantly different

between fish fed 25 and 75 % chicken. All other treatments were significantly

different from each other (see Fig. 3.6b). Worm-fed fish had the greatest δ13C

discrimination, followed by fish fed 25 and 50 % chicken, fish fed 100 % chicken

and fish fed 75 % chicken. Fish fed pure Artemia had a negative discrimination of

δ13C (see Fig. 3.6b), as their tissue δ13C was more negative than the Artemia δ13C,

although fish tissue δ13C may still have been increasing towards diet δ13C.

There was a significant effect of temperature on δ15N discrimination

(F1,39 = 41.40, p < 0.01), but not on δ13C discrimination (F1,39 = 6.13, p > 0.05) for

fish fed pure diets of chicken or Artemia and reared at 16°C or 24°C. Tissue δ15N

discrimination was greater at 16°C than at 24°C for both chicken and Artemia fed

fish (see Fig. 3.6a). Tissue δ13C discrimination was the reverse however, with

greater or more positive discrimination at 24°C than at 16°C (see Fig. 3.6b).

Tanks had a significant effect on the discrimination factors of δ13C

(F5,49 = 2.88, p < 0.05), but not δ15N (F5,49 = 1.93, p > 0.05) for fish fed mixed

diets. However the differences detected were not biologically meaningful, similar

to when tanks effects were detected for δ13C of fish fed mixed diets above.

104

Figure 3.6 Average discrimination factors (Δ ± SE ‰) of (a) δ15N and (b)

δ13C for fish fed worms (W), Artemia (A), chicken (C) or a mixture of chicken

and Artemia (C:A; 25:75, 50:50, 75:25) after 85 days rearing (116 days for worm-

fed fish) at 16°C (filled bars) or 24°C (open bars). Note: * denotes significant

differences between diet treatments when no differences were found among other

treatments; letters denote groups of similar discrimination factors for diet

treatments when all others are significantly different. Temperature significantly

affected δ15N discrimination but not δ13C discrimination.

Diet

W A 25:75 50:50 75:25 C-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

W A 25:75 50:50 75:25 C0

2

4

6

8

10

Δ15 N

Δ13 C

a)

b)

**

a,b

aa,b

b

105

Mixing models

The calculated δ13C and δ15N of fish muscle for fish fed mixed diets (25:75, 50:50

and 75:25) were more accurate, or closer to the measured values, when elemental

concentration was taken into account than when it was not (see Table 3.6). There

was one exception where the calculated δ15N of fish tissue was more accurate

when elemental concentration was not taken into account and this was for fish fed

25 % Artemia and 75 % chicken. When elemental concentration of diets was

accounted for all calculated δ13C and δ15N were within one standard deviation of

the measured mean values of isotopes of fish muscle. However, when elemental

concentration of diets was ignored, calculated δ13C was outside of one standard

deviation but within two standard deviations of the measured mean δ13C of fish

muscle. When elemental concentration of diets was ignored for δ15N, only one

diet mix (75:25) was outside of one standard deviation of measured mean δ15N of

fish muscle, but it was within two standard deviations.

106

Table 3.6 Expected δ13C and δ15N of fish muscle calculated with and without taking elemental concentration ((C), (N)) into account, and

measured isotopic signatures (mean ± SD) for fish reared for 85 days at 24°C and fed mixed diets of chicken (C) and Artemia (A) in varying

proportions by mass (C:A; 25:75, 50:50, 75:25). Note we report standard deviation here so that we can discuss the distribution of the data about

the sample/measured means.

δ13C (‰) δ15N (‰)

Diet Calculated:

without (C)

Calculated:

with (C)

Measured Calculated:

without (N)

Calculated:

with (N)

Measured

25:75 -19.62 -19.83 -20.06 ± 0.32 12.45 11.96 13.0 ± 1.0

50:50 -20.19 -20.45 -20.68 ± 0.24 11.68 11.14 10.8 ± 0.9

75:25 -20.77 -20.94 -21.11 ± 0.19 10.91 10.56 10.1 ± 0.5

107

Discussion

Experimental effects of temperature, diet and time

Fish condition, δ15N and δ13C of fish muscle changed over time and were affected

by diet and temperature. The effect of temperature on fish condition depended on

diet. In general, fish reared at 24°C had faster isotope incorporation, as isotopes

changed more over the duration of the experiment. However, diet affected

isotopic signatures in different ways with δ13C decreasing for fish fed chicken and

increasing for fish fed Artemia. Muscle δ15N decreased over time for fish fed

chicken but was variable over time for fish fed Artemia.

Muscle δ15N of fish fed chicken and reared at 24°C showed the classic

exponential pattern (e.g. Hesslein et al., 1993; Guelinckx et al., 2007). This

exponential pattern likely reflects isotope incorporation by fish muscle during

growth periods over the summer time, when food is abundant and the water is

warm. The half life was 27.2 days which is similar to the finding of Guelinckx et

al. (2007), who found a half life of 27.8 days for δ15N in muscle tissue of the sand

goby Pomatoschistus minutus (Pallas, 1770). It is noted that the sizes of the fish in

both experiments are also comparable, as are the feed rates (approximately 5 %

body weight per feed in this experiment; 3 % body weight in Guelinckx et al.

(2007)) and experimental durations. However P. minutus were reared at 17°C and

A. forsteri were reared at 24°C. It would be expected that the half-life would be

shorter in this experiment than for P. minutus, as isotope turnover is generally

faster at warmer temperatures (Chapter 2, Bosley et al., 2002; Bloomfield et al.,

2011) and A. forsteri were reared in warmer waters. However, the temperatures

used in this experiment and that by Guelinckx et al. (2007) represent summer

108

temperatures for habitats of the respective experimental fishes. The similarities of

half lives for δ15N in muscle between A. forsteri and P. minutus at different

temperatures highlights the need to experimentally determine isotope

incorporation rates for individual species at appropriate temperatures.

The change over time in muscle δ15N of fish fed Artemia was smaller than

the difference in δ15N between the worms and Artemia feeds. Muscle δ15N of fish

fed Artemia varied unpredictably over time and this may be a reflection of

variable δ15N of Artemia over time interacting with fish condition. The condition

of Artemia-fed fish was generally poor and fasting, or poorly nourished, animals

are known to increase in δ15N (Hobson et al., 1993; Gaye-Siessegger et al., 2004b;

Kelly and Martínez del Rio, 2010). Therefore the δ15N of fish fed Artemia was

potentially being influenced by two opposing factors to create variable δ15N over

time: diet δ15N (which was variable over time) and poor nourishment (which is

likely to increase δ15N). However, Guelinckx et al. (2007) also conducted a

starvation experiment on P. minutus and found no change in δ15N of fish muscle

after 20 days of starvation. Aldrichetta forsteri reared on Artemia for 85 days at

16°C were on average very similar in δ15N to fish fed worms for 116 days, and

fish reared at 24°C on Artemia were only slightly lower in δ15N. Therefore, these

results appear to be supportive of no change in δ15N of fish muscle during

starvation or poor nourishment periods.

Fish muscle δ13C values were generally higher at warmer temperatures

than at cooler temperatures for respective diet treatments. This is consistent with

increased metabolism at warmer temperatures, such that fewer lipids are stored or

more are metabolised increasing δ13C values. Tissues with a higher δ13C value are

less likely to have high lipid content than comparable tissues (i.e. reared on the

109

same diet) because lipids are depleted in 13C (DeNiro and Epstein, 1977). The

lower lipid content of fish tissue for fish reared at 24°C is supported by lower C:N

ratios, also indicating low lipid content (Post et al., 2007). The temperature effect

found here on δ13C values of muscle reflects increased metabolism at higher

temperatures and is supported by differences in condition (Fulton‟s K) and C:N

ratios.

There was also an effect of diet on fish muscle δ13C, with fish fed Artemia

increasing in δ13C and fish fed chicken decreasing in δ13C over time. The increase

in muscle δ13C of fish fed Artemia was expected as the feed δ13C of Artemia was

higher than that of worms. Tissue δ13C of fish fed chicken decreased and this was

not expected as δ13C of chicken and worms were similar (Fig. 3.1). The decrease

in δ13C over time of chicken-fed fish muscle shows fish were storing lipids on the

chicken diet, which is supported by higher C:N ratios and Fulton‟s K. The C:N

ratios of fish fed chicken for 85 days were also higher than C:N of fish fed worms

for 116 days, indicating that there were more lipids in fish fed chicken (Post et al.,

2007). These δ13C results reflect the nutritional status of the fish to a certain

degree, with fish fed chicken storing lipids and decreasing in δ13C. However, fish

muscle δ13C was clearly still changing after 85 days rearing on the two diets,

chicken and Artemia, and this appears to be related to condition of the fish as well

as dietary δ13C values.

Isotope discrimination factors

Discrimination of δ13C was not significantly affected by temperature, however,

discrimination of δ15N was affected by temperature and it was 1.15 ‰ less at

24°C than at 16°C. Fractionation, or the difference in isotopic concentration

between reactants and products, is smaller at warmer temperatures than at colder

110

temperatures due to kinetic effects on chemical reactions. Therefore tissue-diet

isotope discrimination should also be smaller at warmer temperatures than at

colder temperatures, as discrimination is the sum of many chemical reactions. The

decrease in δ15N discrimination with increasing temperature is, therefore, likely to

be due to kinetic effects on chemical reactions. The magnitude of the effect of

temperature on δ15N discrimination reported here is similar to that found by

Barnes et al. (2007) and Power et al. (2003). Barnes et al. (2007) found a 0.126 ‰

decrease in δ15N discrimination for every 1°C increase in temperature for

European sea bass Dicentrarchus labrax (L. 1758). The effect found by Power et

al. (2003) was -0.16 ‰ δ15N discrimination for every 1°C increase in temperature

for Daphnia magna Straus, 1820. The lack of a significant effect of temperature

on δ13C discrimination may be due to temperature only influencing δ13C of

Artemia-fed fish as mentioned above. Barnes et al. (2007) found an effect of

temperature on δ13C discrimination, however, this was accounted for by lipid

content of tissues, and C:N ratios of their fish were higher than ours ( > 4). It is

not possible to draw a numerical conclusion on the magnitude of temperature

effects on isotopic discrimination from this experiment because not all groups

were in equilibrium with their diets after 85 days. It can be said, however, that the

effect of temperature on δ15N discrimination is less than -0.14 ‰ per 1°C increase

in temperature and that there is no effect on δ13C discrimination.

There was an effect of diet on isotope discrimination, however, the

variation in discrimination did not show a linear relationship with feed mixtures

but may reflect complex dietary effects. Discrimination of δ15N was greater for

fish fed high proportions of chicken (50, 75, and 100 %) than fish fed pure

Artemia. The concentration of nitrogen, and probably protein, was higher in diets

111

with greater chicken content therefore fish may have been catabolising more

proteins so that fish tissue was enriched in 15N (Gaye-Siessegger et al., 2004a;

Tsahar et al., 2008). However, fish fed the 25:75 mixture had the greatest δ15N

discrimination, although dietary % N was lower. This may be due to fish

experiencing protein deficiency in their diet and recycling nitrogen within their

bodies with some catabolism of proteins so that 15N is more enriched (Gannes et

al., 1998 and references therein; Gaye-Siessegger et al., 2004a). Fish fed high

proportions of Artemia were in the worst condition and had the lowest C:N ratios,

indicating that they were poorly nourished compared to fish fed high proportions

of chicken. However, this does not explain the Artemia-fed fish having lower δ15N

discrimination; in fact if fish were in a poorer condition then their δ15N

discrimination should have gone up, as it did for fish fed the 25:75 mix. It could

be that fish fed pure Artemia were sparing their protein such that carbohydrates

were being used for energy and proteins were being conserved for growth only

(Shiau and Peng, 1993; Kelly and Martínez del Rio, 2010), leading to a lower

δ15N discrimination. The low C:N ratios of Artemia-fed fish support this idea, as it

appears that fish fed pure Artemia were not storing lipids.

Discrimination of δ13C was less than 1.16 ‰ for experimental diets and

there was no significant difference among fish fed any amount of chicken in their

diet. Unfortunately, δ13C of fish fed pure Artemia or pure chicken was still

changing after 85 days rearing, limiting the conclusions that can be made

regarding δ13C discrimination. The δ13C discrimination of worm fed fish, which

were reared for 116 days, was 1.95 ‰ or approximately double that of fish fed

pure chicken. To further confuse the issue, δ13C of worms and chicken were

similar, although worms had 3.69 % less nitrogen than chicken and 1.68 % more

112

carbon. It could be that there is an interaction occurring between carbon and

nitrogen elemental concentration such that fish fed chicken with their higher

dietary nitrogen, and consequently protein, catabolise more protein for energy.

Catabolising protein allows fish fed chicken to store more lipids and decrease

their overall δ13C signature compared to worm-fed fish. Worm-fed fish may have

catabolised less protein for energy and more carbohydrates and have less

carbohydrates and lipids stored, leading to greater δ13C discrimination. Increasing

C:N ratios of fish fed increasing proportions of chicken supports the idea that fish

fed pure chicken stored more lipids, leading to a lower δ13C. Research into

enzyme activity may help unravel variation in isotopic discrimination by

indicating which metabolic processes are dominating, and therefore improve

dietary back-calculation (Gaye-Siessegger et al., 2005). Until such data are

thoroughly tested researchers may need to incorporate more uncertainty regarding

δ13C discrimination to reduce the likelihood of making erroneous conclusions of

proportional source estimations.

Elemental concentration

The linear relationships between the isotopic signatures of fish tissue and the

elemental concentration of the diet further support the importance of elemental

concentration of diet in determining the isotopic signature of consumers. The

discrimination factors for diets did not show a linear relationship with proportion

of feed or elemental concentration, showing that discrimination factors could not

be predicted by diet mixing. However, the accuracy of calculated isotopic

signatures for fish tissue using the concentration dependent mixing model showed

how important elemental concentration is and that it may be a primary

determinant of isotopic signatures of consumers. This supports previous work

113

where elemental concentration of diet was a primary factor in determining

isotopic signatures of consumers (Pearson et al., 2003; Mirón et al., 2006).

Field study implications

The aim of this study was to test and quantify the effects of environmental

variability in ambient temperature and elemental concentration of diet sources on

isotope discrimination and incorporation rates of δ15N and δ13C in A. forsteri.

Temperature affected isotopic discrimination of δ15N by less than 0.14 ‰ per 1°C.

Although this does not seem to be a large effect size, water temperatures can vary

by 10°C or more between summer and winter (e.g. Jones et al., 1996). If

temperature effects on δ15N discrimination are ignored when back-calculating

diets erroneous estimates of source contributions may be made, particularly if

comparing summer and winter diets. The best estimate of discrimination for δ15N

by A. forsteri is 5.19 ± 0.74 ‰ at 24°C (asymptote of chicken fed fish at 24°C

minus chicken δ15N ± 1SD). This value is very similar to that found for

Acanthopagrus butcheri (5.07 ± 0.66 (1SD) ‰) (Chapter 2, Bloomfield et al.,

2011), another omnivorous fish, and it is within a range of estimates for other

organisms (Adams and Sterner, 2000; Gaston and Suthers, 2004; Connolly et al.,

2005a; Mill et al., 2007). However, this value is higher than the 3.4 ‰

recommended by Post (2002), re-enforcing the need to experimentally quantify

discrimination factors for study species whenever possible (Gannes et al., 1997;

Martínez del Rio et al., 2009). It is recommended to use 5.19 ± 0.74 (1SD) ‰ at

24°C for δ15N discrimination by A. forsteri in field settings and to account for

temperature effects of ambient water by adjusting the discrimination factor by

increasing it by 0.14 ‰ per 1°C less than 24°C.

114

The results for δ13C discrimination largely agree with that of Post (2002),

who recommended applying a 0-1 ‰ discrimination. The estimates of δ13C

discrimination from experimental diets were all below 1.16 ‰ and decreasing.

However, the discrimination found for fish fed worms was comparatively large,

1.96 ‰. This result is less than other δ13C discrimination factors reported in the

literature with δ13C discrimination being recorded as large as 3.7 ‰ for Australian

mado Atypichthys strigatus (Günther, 1860) (Gaston and Suthers, 2004).

Therefore, to include uncertainty in δ13C discrimination it is recommended to use

1.15 ± 0.67 ‰ (grand mean of fish fed chicken or worms at all temperatures ±

SD) for A. forsteri. There was no effect of temperature therefore discrimination of

δ13C does not need to be adjusted for temperature differences.

Isotope incorporation rates can be extremely useful for field studies to

determine when an animal has arrived in a new habitat that differs in basal

isotopic signatures from its previous habitat by interpolating the isotopic data and

the rate of change (Herzka et al., 2002). It was found that temperature affected

isotopic incorporation rates, however it was only possible to quantify

incorporation rates adequately for δ15N of fish fed chicken at 24°C. Therefore the

best estimate of time over which isotopes reflect dietary composition is

approximately 54.4 days during summer (at 24°C). Isotopic incorporation rates

are known to be affected by other factors as well as temperature; including age, or

body size, and ration intake (Herzka and Holt, 2000; Trueman et al., 2005;

Carleton and Martínez del Rio, 2010). Therefore these results are likely only

applicable to juvenile A. forsteri less than 4.5 g or 65 mm SL. More research into

how to quantify or adjust for factors that may affect incorporation rates and

115

isotopic discrimination that cannot directly be measured (such as condition or

ration intake) is needed to improve outcomes of field studies.

These results clearly show that elemental concentration should be

accounted for in mixing models and that Phillips and Koch‟s (2002) concentration

dependent mixing model holds up well when tested against experimental data.

However, the regressions of fish muscle isotopes against elemental concentration

of diet may not be widely applicable to other situations. The concentration of

nitrogen was higher in chicken than in Artemia, however the δ15N was higher in

Artemia than in chicken and this would more likely be reversed in nature. The

reason for the reversal of trends in nitrogen isotopes and elemental concentration

in nature is that animal tissue usually has a higher nitrogen concentration than

plant matter and animal tissue usually increases in δ15N up a food web. Therefore

animal tissue δ15N should be positively correlated with concentration of nitrogen

in food sources. The negative correlation found here between carbon

concentration in food and δ13C of fish muscle may be applicable to other

situations. Animals eating a diet higher in carbon concentration are more likely to

store lipids (Gaye-Siessegger et al., 2005) and lipids are known to be depleted in

13C, leading to more negative δ13C values of tissues. Therefore a negative

relationship between carbon concentration of diet and δ13C of tissues is likely to

be found in nature. However, further tests of this theory across a range of

elemental concentrations and isotopic signatures are needed before it could be

applied to field samples.

This study has shown the importance of using elemental concentration in

mixing models and that diet and temperature affect isotopic discrimination and

incorporation rates. However, there are still other factors that are known to affect

116

isotopic discrimination and incorporation rates that cannot easily be measured in

the field (e.g. ration intake Barnes et al., 2007). Methods to enable researchers to

account for factors that cannot directly be measured need to be further developed

to enable the improvement of isotope field studies, such as using lipogenic

enzyme activity to adjust for lipid intake (Gaye-Siessegger et al., 2005). It is

suggested that future research focus on developing methods to improve isotopic

discrimination estimates by analysing key enzyme activity simultaneously with

isotopic signatures.

Acknowledgments

This experiment was carried out under the animal care guidelines of the

University of Adelaide (Animal Ethics Permit number S-074-2007). Fish were

collected under Fisheries Management Act 2007 permit numbers 9902145 and

9902146 from the Department of Primary Industry and Resources South

Australia. Funding was provided by the ARC Linkage grants program and the Sir

Mark Mitchell Foundation. Stable isotope analyses were conducted by

R. Diocares at Griffith University. A. Cosgrove-Wilke, J. Livore, T. Barnes and

S. Woodcock are gratefully thanked for their assistance in collecting and caring

for fish and for lab assistance. We are also grateful to J. Stanley for his assistance

in running the aquarium room.

117

Chapter Four: Stable isotopes allude

to separate ecological niches of two

omnivorous, estuarine fishes

South West River mouth, Kangaroo Island, October 2008.

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Chapter 4 Preamble

This chapter is a co-authored paper, with intention to publish in a peer-reviewed

scientific journal. Bronwyn Gillanders and Travis Elsdon are co-authors, therefore

it is written in plural.

In this chapter I conceived the sampling design and researched the techniques

used. I received intellectual input on field sampling and funding assistance from

Bronwyn Gillanders and Travis Elsdon. I collected the samples, with assistance

from others (see acknowledgments), and prepared all the samples for analyses. I

did all of the statistical analyses and wrote the manuscript with input from co-

authors.






119

Stable isotopes allude to separate ecological niches of two

omnivorous, estuarine fishes

Abstract

Stable isotopes were used to investigate ecological niches, as isotopes of nitrogen

and carbon reflect environmental and dietary attributes of niches. We investigated

the isotopic niches of two common, omnivorous fishes that frequently inhabit

estuarine areas together: Acanthopagrus butcheri and Aldrichetta forsteri. We

further studied the autotrophic sources that these fishes relied on. Although the

fishes relied on similar autotrophic sources in some estuaries, they were feeding at

different trophic levels. Isotopic niches of A. butcheri and A. forsteri did not

overlap in any of the estuaries sampled and this is likely due to interspecific

competition, potentially causing habitat partitioning. Our results support the

theory that no two species can occupy the same ecological niche. Isotopic niches

show potential as a tool for a better understanding of ecological niches.

Introduction

Ecological niches are hard to define and difficult to measure, yet they are central

to ecological theory. A niche was defined by Hutchinson (1957) as an abstract set

of points in multi-dimensional space that define the boundaries within which a

species lives. The multiple dimensions or axes represent environmental variables

and therefore the set of points define the environmental boundaries in which a

species persists. Ecologists have struggled with measuring niches due to the large

number of environmental variables that can make up the multiple dimensions.

Hutchinson (1978) later made an important distinction between two sorts of

120

dimensions: scenopoetic and bionomic. Scenopoetic dimensions set the stage of

physical and chemical variables in the environment within which an animal lives.

Bionomic dimensions relate to resources that an animal uses to sustain its

existence. Newsome et al. (2007) suggested that stable isotopes in animals,

particularly δ13C and δ15N, can be used to investigate ecological niches as an

animal‟s chemical composition is a result of what it has been eating (bionomic)

within its environment (scenopoetic).

Stable isotopes of carbon (δ13C) vary among primary producers at the base

of the food web due to varying chemical reactions and physical processes that

change the ratios of 13C to 12C in molecules (Marshall et al., 2007). There are stark

differences in the chemical reactions and physical processes of photosynthesis

between C3, C4, and CAM plants that have allowed ecologists and

paleoecologists to investigate the relative proportions of these plants in diets of

modern and historical animals (see Schwarcz, 1991; Koch, 2007). The δ13C of

algae can vary between benthic and pelagic communities due to boundary effects

in the benthos (Fry, 1996; Jennings et al., 1997; Smit, 2001). Planktonic δ13C can

vary spatially due to temperature effects (Wong and Sackett, 1978), causing

variation in δ13C between inshore and offshore food webs (Fry, 1983; Hobson,

1999) and at the larger scale of latitudes (Rau et al., 1982; Johnston and Kennedy,

1998). Carbon isotopic signatures are also known to vary along estuaries with

salinity gradients (e.g. Deegan and Garritt, 1997; Leakey et al., 2008; Hoffman et

al., 2010). Therefore δ13C can reflect dietary and environmental aspects of niches.

The heavy stable isotope of nitrogen (15N) is preferentially retained in

animals over the lighter isotope (14N) so that animal tissue is enriched in 15N

compared to its diet (DeNiro and Epstein, 1981). Enrichment in 15N occurs every

121

time an animal eats, therefore δ15N of animal tissue increases as trophic level

increases (Minagawa and Wada, 1984). The increase in δ15N with increasing

trophic level has been used to determine trophic position of animals within food

webs (e.g. Davenport and Bax, 2002; Jaschinski et al., 2008a) and therefore gives

us dietary information. Stable isotopes of nitrogen (δ15N) are also influenced by

anthropogenic activities (Heaton, 1986) and have been used to trace sewage inputs

through aquatic food webs (e.g. Gaston et al., 2004; Hadwen and Arthington,

2007). Plankton δ15N signatures are also known to vary spatially (Gruber and

Sarmiento, 1997; Popp et al., 2007), therefore δ15N can also provide

environmental information.

It was once thought that no two species could occupy similar ecological

niches due to competition for resources (reviewed by Hutchinson, 1978) and this

is implicit in Hutchinson‟s niche definition (1957). However, niches can overlap

to a certain degree when organisms share habitat or occur in similar

environmental conditions (e.g. Aguilera and Navarrete, 2011; Silva-Pereira et al.,

2011). Two fishes that commonly occur in estuaries in southern Australia are

black bream (Acanthopagrus butcheri) and yellow-eye mullet (Aldrichetta

forsteri) (Potter and Hyndes, 1994; Jones et al., 1996; Norriss et al., 2002). Black

bream and yellow-eye mullet are both omnivorous (Sarre et al., 2000; Platell et

al., 2006), euryhaline fish with a wide distribution across southern Australia

(Kailola et al., 1993). They have both been described as opportunistic in their

feeding behaviour, suggesting that they eat whatever is readily available (Sarre et

al., 2000; Platell et al., 2006). Despite their shared environmental requirements

and opportunistic feeding behaviour, black bream and yellow-eye mullet persist

together in estuaries as two of the most abundant species. Therefore the ecological

122

niches of black bream and yellow-eye mullet should be largely different, but may

overlap.

Using stable isotopes we aimed to investigate the ecological niches of

black bream and yellow-eye mullet and their overlap. We calculated metrics of

stable isotopes: 1) the range of δ15N of a species, and 2) niche width, or area of

isotopic variability, per species (Layman et al., 2007; Quevedo et al., 2009). The

range of δ15N of a consumer tells us the range of trophic levels at which the

species feeds (i.e. if it is strictly herbivorous or omnivorous). Niche width, or area

of isotopic variability, has previously been quantified by total area of the convex

hull. Total area of the convex hull refers to the area enclosed within lines drawn

between the extreme most values of isotopes per species (Layman et al., 2007).

The drawback to these metrics is that they are sample size dependent and can

increase with increasing sample size (Jackson et al., 2011). This is particularly

problematic for total area of the convex hull as it has been used for niche width

analyses among species and populations with varying numbers of samples (e.g.

Darimont et al., 2009; Olsson et al., 2009). Jackson et al. (2011) recently

described a new method for estimating isotopic niche width, which is not as

sensitive to sample size. We analysed the δ15N range and isotopic niche width

using the methods of Jackson et al. (2011) for black bream and yellow-eye mullet

in four estuaries.

We aimed to determine the autotrophic sources that black bream and

yellow-eye mullet rely on in the estuaries sampled. Black bream and yellow-eye

mullet both feed opportunistically and are likely to consume the most abundant or

readily available prey. Therefore the diet of these fishes should reflect the

autotrophic sources that are contributing the most nutrients and energy to the

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ecosystem. However, if black bream and yellow-eye mullet occupy different

ecological niches, they may also rely on different autotrophic sources with

varying degrees of overlap.

Methods

Estuaries

Four estuaries (Chapman, Harriet, Onkaparinga, and South West River) in South

Australia were sampled for black bream, yellow-eye mullet, and autotrophic

sources (see Fig. 4.1). The Onkaparinga estuary was the largest sampled in this

study and was sampled at two locations 6.5 km apart that varied in salinity (see

Table 4.1). The Onkaparinga River was connected to the sea at the time of

sampling (see Table 4.1), and the tidal influence can extend 10.5 km inland

(Department for Environment and Heritage, 2007a). The Onkaparinga estuary has

a main river channel, with tidal flats nearer the sea and saltmarshes fringing the

channel. The upper Onkaparinga has some riparian vegetation of trees and shrubs

up to the river bank where the river channel is narrow (< 20 m) and can be

shallow, although there are some deep holes with fallen branches and rocky

sections, creating complex habitat structure. The lower Onkaparinga site had a

broader channel (approximately 70 m wide), which was devoid of subtidal

structure except for small patches of seagrass. There were waste water sludge

lagoons adjacent to the Onkaparinga River at the time of sampling, very close to

the lower site, that were known to flood occasionally and spill over into the river.

These sludge lagoons were decommissioned not long after field work for this

research was completed, however they may still be leaching.

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Figure 4.1 Map showing the location of estuaries sampled in South Australia,

Australia. Empty circles = open estuaries; filled circle = closed estuary at time of

sampling.

Adelaide

Onkaparinga River

Chapman River

South West River

Kangaroo Island

Harriet River

Australia

Enlarged area

South Australia

200 km

138ºE136ºE 137ºE

36ºSN

125

Table 4.1 Estuarine and catchment information: size, estuarine type, salinity, temperature and status of estuarine opening when black

bream (BB) and yellow-eye mullet (YEM) were collected.

Estuary and site Length

(km)

Channel

area (ha)

Catchment

area (km2)

Estuarine type Salinity

(‰)

Temperature

(°C)

Status of estuarine

connectivity with the

sea

Chapman 2.32 6.52 731 Wave dominated1 16 18 (BB)

21 (YEM)

Closed

Harriet 1.72 7.82 1521 Wave dominated1 16 (BB)

6 (YEM)

17 (BB)

20 (YEM)

Open, small connection

Onkaparinga

lower

11.01 49.32

5541 River dominated,

with wave-

dominated delta1

32 18 Open, large opening,

tidally influenced

Onkaparinga

upstream

13 (BB)

14 (YEM)

22.5 (BB)

18 (YEM)

South West 1.62 2.82 1551 Wave dominated1 3 20 Open, small connection

1(Department for Environment and Heritage, 2007a, b)

2(Department of Environment and Natural Resources, 2011)

126

Chapman River, on Kangaroo Island, is smaller than the Onkaparinga (see

Table 4.1) and is surrounded by a conservation park, with riparian vegetation

along most of its length. The channel was relatively wide (> 30 m) near the mouth

where it was sampled and can be deep (> 2 m), with subtidal structure created by

fallen trees and vegetation. There were also shallower sandy sections where

yellow-eye mullet were caught. Chapman River seasonally closes to the ocean

over summer and it was closed at the time of sampling (see Table 4.1). Harriet

and South West Rivers, also on Kangaroo Island, seasonally close to the ocean

too; however both were open at the time of sampling (see Table 4.1). Harriet

River was the widest estuary sampled with the channel being up to 100 m wide

and fringed by riparian vegetation along much of its length, although this

vegetation band was quite narrow (< 10 m). The South West River was the

shortest estuary sampled and is quite narrow along most of its length (< 25 m)

with a shallow pool near the mouth (up to 80 m wide). There was riparian

vegetation fringing much of the river as it flowed through a National Park. South

West River is also higher above sea level compared to the other estuaries sampled

and is known to remain fresher for longer and is generally shallower.

Fish and autotroph collection

Fish and autotroph samples were collected in October 2008 from the four

estuaries. Fish were collected by seine net (5-20 m long; 19 mm mesh size) or

handline within estuaries. Fish were collected from sites within estuaries where

they had previously been sampled as part of other research projects. A minimum

of five fish per species per estuary/site were caught and euthanized in an ice water

slurry. Fish were kept on ice in the field and frozen on return to the laboratory.

127

Autotroph samples were collected at the same site and time as fish. Plant

samples were collected based on a visual assessment of whether plants were able

to directly contribute organic matter to the estuary. Plants were able to directly

contribute organic matter to estuarine waters if they grew within the immediate

catchment, including within the water body itself. Plants that were able to

contribute organic matter to the estuary had samples of leaves or photosynthetic

material collected. Triplicate samples per plant species were collected, with

individual plants used as replicates where possible. Macroalgae were collected

from within seine nets or if found on the shore within estuaries. It was not

possible to collect triplicate samples of macroalga species due to the nature of the

estuaries sampled, with most estuaries being small with minimal hard substrata for

attachment of macroalgae, however samples were analysed in duplicate whenever

possible. Terrestrial and aquatic plants, including macroalgae, were identified to

lowest taxonomic resolution possible (usually species, but occasionally genus

with the exception of saltmarshes). Saltmarshes could only be identified to

subfamily (Salicornioideae), as no flowers were present at the time of sampling.

Epiphytes and periphyton were collected from plants and other macroalgae, rocks

and other hard substrata, and were usually collected as one sample to be analysed

in triplicate for stable isotopes. Plant samples were bagged individually and put on

ice initially, being frozen later that day.

Particulate organic matter (POM) samples were collected using a

plankton-net with 25 μm mesh, ring size 25 cm in diameter, with a cup attached to

the end to collect the sample. The net was pulled through 20 m of water (similar

to Hadwen et al., 2007) being careful not to disturb sand and mud from the

bottom. Three samples were taken over separate 20 m lengths of the estuary

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where possible. The net was rinsed so that as much organic matter as possible was

washed into the cup. The water in the cup was collected into an opaque container

and put on ice until the POM (>25 µm) could be vacuum filtered onto pre-

combusted GF/F filter paper later that day.

Microphytobenthos was collected in triplicate when sand or mud was

readily available (at all sites except for the upper Onkaparinga where it was very

rocky). The top few centimetres of sediment were collected over a 1 m2 area into

an opaque container and kept below 4°C until processing. Water temperature and

salinity were measured at each site using a YSI sonde (model 556 MPS).

Sample preparation

Fish were defrosted, weighed (mass, g) and measured (total length, mm) before

having dorsal muscle samples taken. Fish muscle samples were freeze-dried

before being ground to a powder using an agate mortar and pestle. Plant samples

were rinsed with ultrapure water. Plant and filtered POM samples were oven dried

at 80°C for 48 hrs. Plant samples were ground using one of three methods (ball

mill, coffee grinder or agate mortar and pestle) depending on their volume and

fibrous nature. Oven-dried POM was scraped off filters and acidified with 1M

HCl in glass vials. Acid was added drop by drop until effervescence ceased

(Carabel et al., 2006), after which samples were allowed to dry under a fume hood

for several days. Acidified POM was ground with an agate mortar and pestle.

Macroalgae samples were not coralline and therefore did not require acidification.

Microphytobenthos (MPB) was sieved sequentially through 1 mm,

500 μm, and 53 μm sieves into a bucket and allowed to stand for several days in a

dark cool room until the water was clear (Melville and Connolly, 2003). The

supernatant was poured off and the remaining sample was resuspended and mixed

129

with Ludox TM-50 (colloidal silica) to a density of 1.27 g.mL-1 (Hamilton et al.,

2005). The mixture was centrifuged at 10 g for 10 mins such that diatoms were

suspended in the top layer and detritus was compacted to the bottom. The diatom

fraction was extracted and rinsed onto 5 μm fabric. It was then oven-dried for

48 hrs at 80°C. Dried samples were ground with an agate mortar and pestle.

Lipids were not extracted from any samples. All samples were weighed into tin

capsules for stable isotope analyses.

Stable isotope and elemental concentration analyses

Samples were analysed by a GV Isoprime Mass Spectrometer coupled to a

Eurovector elemental analyser 3000 at Griffith University, Queensland, Australia.

International and internal laboratory standards (N: Ambient Air, IAEA-305a,

C: ANU Sucrose, Acetanilide, Working standards: 'Prawn', „Flour‟) were run in

parallel with fish and plant samples to enable calibration of results. Average

precision of the mass spectrometer was 0.06 ‰ for δ13C and 0.23 ‰ for δ15N

(1SD), with average accuracy of 0.01 ‰ of δ13C and 0.10 ‰ for δ15N (average

deviation from known value). Average precision of the elemental analyser was

0.62 % for carbon and 0.27 % for nitrogen (1 SD), with average accuracy of

0.24 % for carbon and 0.05 % for nitrogen (average deviation from theoretical

value).

Data analysis

Fish condition was calculated using Fulton‟s K. Size (length and mass) and

condition of black bream and yellow-eye mullet among estuaries/sites were

compared in one-way ANOVAs, as size and condition of fish can influence

isotopic signatures (Davenport and Bax, 2002; Melville and Connolly, 2003;

130

Gaye-Siessegger et al., 2007). When significant differences were found, post-hoc

comparisons were done using Student-Newman-Keuls (SNK) tests.

Stable isotope values of carbon (δ13C) were not mathematically corrected

for lipid content. Post et al. (2007) recommends correcting for lipids in fish

samples when C:N ratios are larger than 3.5 and all fish samples had C:N ratios of

3.5 or less (not reported here). It is not logical to correct autotroph samples for

lipids as fish, and other potential prey items, ingest whole items without

discriminating against lipids, which would be digested and assimilated into fish

tissue.

Regression analyses were done to see if there were significant

relationships between isotopes and fish sizes, separating the Onkaparinga from

other estuaries for δ15N analyses due to strong 15N enrichment. Isotopic

composition (δ15N and δ13C) of black bream and yellow-eye mullet across

estuaries/sites sampled were analysed in a two-factor permutational multivariate

analysis of variance (PERMANOVA, Anderson, 2001), with fish species and

estuary as fixed factors, to see if isotopic signatures varied between fishes and

among estuaries. Data were not transformed, resembled using Euclidean similarity

distance matrices, and permutations were unrestricted.

The range of δ15N values was calculated for each fish species per estuary.

Isotopic niche width was calculated using the SIAR package (version 4.1 Parnell

and Jackson, 2011) in R (R Development Core Team, 2011). Standard ellipse

area, corrected for small sample size (SEAc), was calculated for each species in

each estuary (Jackson et al., 2011). SEAc was analysed using ANOVA to test if

the isotopic niche width varied between fishes with estuaries as replicates. The

Bayesian standard ellipse area (SEAb) was calculated for each fish species per

131

estuary to obtain a better estimate of isotopic niche width and to estimate which

fish species is more likely to have a larger isotopic niche (Jackson et al., 2011).

Isotopic niche overlap was also calculated using the SIAR function “overlap”,

with step = 1.

Proportional contributions of autotrophic sources to fish diets were

estimated using SIAR (version 4.1 Parnell and Jackson, 2011). The

“siarmcmcdirichletv4” function was used. This function runs a Markov Chain

Monte Carlo (MCMC) method on stable isotope data with a Gaussian likelihood

assumed for target values and a Dirichlet-distributed prior on the means of sources

(Parnell et al., 2010). This function calculates feasible solutions of proportional

source contributions, similar to IsoSource outputs (Phillips and Gregg, 2003),

however it uses the uncertainty associated with data inputs in the model. It

incorporates uncertainty in source (autotroph) and „target‟ (fish tissue) isotopic

signatures as well as uncertainty in discrimination corrections. It is important to

incorporate uncertainty in isotopic discrimination (the difference in isotope ratios

between a source and consumer), as discrimination is known to vary among and

within organisms (Chapters 2 and 3, DeNiro and Epstein, 1978, 1981; Elsdon et

al., 2010; Bloomfield et al., 2011) and with environmental factors, such as

temperature (Chapters 2 and 3, Bosley et al., 2002; Barnes et al., 2007;

Bloomfield et al., 2011). SIAR also allows for the use of elemental concentration

of sources in the mixing model, which is known to influence isotopic signatures of

animal tissue (Chapter 3, Pearson et al., 2003; Mirón et al., 2006)5. We used SIAR

5 We acknowledge that MPB and POM are concentrated samples and that their elemental

concentration does not reflect that consumed in nature. However the elemental concentration of MPB was very small (C% = 0.36 ± 0.38 (mean ± 1SD), N% = 0.035 ± 0.036 (mean ± 1SD)). The elemental concentration of POM was larger, and much more variable (C% = 10 ± 6.7 (mean ± 1SD); N% = 1.2 ± 1.1 (mean ±1SD)) however this was still below the carbon concentration of most other sources sampled (grand mean: C% = 37 ± 9.1 (mean ± 1SD)) although the nitrogen concentration was similar (grand mean: N% = 1.5 ± 0.86 (mean ± 1SD)).

132

separately for black bream and yellow-eye mullet for each estuary/site, using

400,000 MCMC iterations with 200,000 burn in and thinning by 100 (see Parnell

et al., 2010). We report modes with 95 % confidence intervals for variance and we

used modes for further data analyses, as recommended by Parnell et al. (2010).

We used different discrimination factors (Δ13C, Δ15N) for black bream and

yellow-eye mullet. Black bream and yellow-eye mullet are omnivores, therefore

they are likely to feed on both plant and animal matter and may receive nutrients

from the same autotrophic source through both plants and animals. As omnivores

black bream and yellow-eye mullet are in a trophic position in between herbivores

(Trophic level (TL) = 1) and carnivores (TL = 2); approximately TL = 1.5. We

used experimentally derived discrimination factors for black bream and yellow-

eye mullet (Chapters 2 and 3, Bloomfield et al., 2011) and added half of the

average discrimination factor across a range of species (Post, 2002) to account for

unquantifiable discrimination by potential prey items. For black bream we used

discrimination factor of Δ13C = 3.50 ± 0.73 (mean ± 1SD) as found in Chapter 2

(Bloomfield et al., 2011) and added half of the average discrimination found by

Post (2002) (0.39 ± 1.3 (1SD) /2 = 0.195 ± 1.3; we retained the variation of the

full average as dividing the average by two does not improve the accuracy) to give

a trophic enrichment factor equivalent to 1.5 trophic levels. On adding the two

discrimination factors together we added the variance, as errors were presumed to

be additive, to give a Δ13C = 3.70 ± 2.03 (mean ± 1SD). We did similar additions

for Δ15N for black bream using the discrimination found in Chapter 2 (Bloomfield

et al., 2011) to give Δ15N = 6.77 ± 1.64 (mean ± 1SD).

Discrimination of δ15N in yellow-eye mullet can be affected by

temperature (Chapter 3), therefore we adjusted Δ15N to the temperature measured

133

on the day of collection and added half of the average discrimination factor found

by Post (2002). We acknowledge that the temperature of the water that fish live in

would have varied over the preceding time period that isotopic signatures of tissue

were incorporated (approx. 54.4 days for yellow-eye mullet; Chapter 3). However,

we did not quantify temperature variation prior to collection and the variance of

the discrimination factor was already reasonably large. We adjusted Δ15N by

0.14 ‰ per 1°C as per the findings in Chapter 3. This resulted in Δ15N ranging

from 7.31 (Chapman) to 7.73 (Onkaparinga) ± 1.72 ‰ (1SD). No affect of

temperature was found on Δ13C (Chapter 3), therefore no temperature adjustments

were made and the recommended value of 1.15 ± 0.67 (1SD) (Chapter 3) was

added to half of Post‟s average to derive Δ13C = 1.35 ± 1.97 ‰, which was

applied across all estuaries/sites for yellow-eye mullet in the SIAR analyses.

SIAR does not cope well with sources that are too similar in isotopic

composition, as it cannot separate their contributions (Parnell et al., 2010; Bond

and Diamond, 2011). Several species of marine macroalgae that were collected in

the Harriet and South West Rivers were similar in isotopic composition so they

were pooled to make one (Harriet) or two (South West River) autotrophic

signatures (see Figs 4.3b & d). Some terrestrial shrubs and reeds (Ficinia nodusa,

Disticus disticus and Carpobrotus rossi) in the South West River were also

similar in isotopic composition and thus were pooled into a „shrubs‟ autotrophic

source. Two grasses were also pooled in the South West River to give one

isotopic signature.

There were two analyses where isotopes of yellow-eye mullet did not fit

well within the mixing polygon for that estuary (the polygon including all sources

from that estuary, accounting for variance (1SD) in isotopic signatures and

134

discrimination): in the Chapman River and at the Onkaparinga lower site. The

analyses for yellow-eye mullet in the Chapman River required fish isotopes to be

further trophically corrected such that yellow-eye mullet were feeding at a trophic

level of two. To correct yellow-eye mullet isotopes for two trophic levels we

added the full average of isotopic discrimination across a range of species, as

found by Post (2002), to the experimentally derived and temperature corrected

trophic discrimination for yellow-eye mullet (Chapter 3). In the Onkaparinga

lower analysis, yellow-eye mullet isotopes were over corrected by applying

1.5 TL discrimination. Therefore we used the experimentally derived and

temperature corrected trophic discrimination for yellow-eye mullet alone, without

adding anything, such that fish were feeding at a trophic level of one.

To determine how similar autotroph relative importance was between

black bream and yellow-eye mullet within an estuary, modes from SIAR outputs

were used to determine Bray-Curtis similarity indices without transforming data.

Results

Fish size and condition

There was a significant difference in the size (length and mass) of fish among

estuaries (black bream total length: F4,24 = 6.06, p = 0.003; mass: F4,24 = 3.68,

p = 0.03; yellow-eye mullet total length: F4,24 = 36.58, p = 0.001; mass:

F4,24 = 25.80, p = 0.001). Black bream caught in the upper reaches of the

Onkaparinga were smaller (length and mass) than black bream caught in all other

estuaries/sites (Figs 4.2a & b). Black bream caught in the South West River were

significantly shorter than black bream caught in the Harriet River (Fig. 4.2b).

There were no other significant differences in size among estuary/site pairs for

135

black bream. Yellow-eye mullet caught in the Harriet River were significantly

larger (length and mass) than yellow-eye mullet caught in all other estuaries/sites

(Figs 4.2a & b). Yellow-eye mullet caught in the South West and Chapman Rivers

were similar in size (Figs 4.2a & b). Yellow-eye mullet caught in the Onkaparinga

did not differ in size between upper and lower sites (Figs 4.2a & b). Yellow-eye

mullet size differed significantly among all other pairs of estuaries/sites (Figs 4.2a

& b). Despite the size differences among estuaries no significant relationships

between fish size (length and mass) and δ13C or δ15N were found (r2 < 0.5) for

either species.

136

Figure 4.2 Fish a) mass (g), b) total length (TL, mm), and c) condition

(Fulton‟s K) of black bream (grey bars) and yellow-eye mullet (white bars)

collected in estuaries/sites. Note: letters denote groups of estuaries where fish

sizes and condition were not significantly different per species; * denotes sites

with significantly different fish sizes from all other sites per species.

Mas

s (g

)

0

10

20

30

40

50

TL (m

m)

0

20

40

60

80

100

120

140

160

Estuary

ChapmanHarrie

t

Onkaparinga Lower

Onkaparinga Upstream

South West

Fulto

n's

K

0

1

2

3

4

*

*

a

a,b

a

a,b

a

a

a

b

*

*

b

c

b

c

c

d

c

d

bc,d b,d b,c c

a

aaaa

a)

b)

c)

137

The condition (Fulton‟s K) of black bream did not differ among

estuaries/sites (F4,24 = 2.21, p = 0.11; Fig. 4.2c). The condition of yellow-eye

mullet, however, did differ among estuaries/sites (F4,24 = 4.18, p = 0.01; Fig. 4.2c).

Yellow-eye mullet caught in the South West River were in significantly better

condition that yellow-eye mullet caught in the Chapman River and Onkaparinga

lower site (Fig. 4.2c). Yellow-eye mullet caught in the Harriet River were in

significantly better condition that yellow-eye mullet caught in the Chapman River.

Fish of both species caught in the Chapman River were in the poorest condition

and fish caught in the South West River were in the best condition.

Fish and autotroph isotopes

A significant interaction between fish species and estuary was found for isotopic

composition (δ13C and δ15N) of fishes, with significant differences in isotopic

composition between black bream and yellow-eye mullet in each estuary (Table

4.2; Fig. 4.3). Yellow-eye mullet were enriched in carbon and nitrogen isotopes in

the Chapman, Harriet, and South West Rivers relative to black bream. However,

black bream were enriched in carbon and nitrogen isotopes compared to yellow-

eye mullet in both upper and lower sites in the Onkaparinga River. Carbon and

nitrogen isotopes of yellow-eye mullet caught in the Chapman, Harriet, and South

West Rivers were similar. Isotopes of black bream were similar between Harriet

and South West Rivers and between Chapman and South West Rivers. Carbon

and nitrogen isotopes of fishes caught in the upper and lower Onkaparinga were

significantly different from other estuaries, as well as between the two sites.

138

Table 4.2 Two factor permutational multivariate analysis of variance

(PERMANOVA) of isotopes (δ13C and δ15N) for black bream and yellow-eye

mullet among estuaries. Bolded numbers indicate significant effects (p < 0.05).

Source of variation df MS p

Estuary 4 77.75 0.001

Fish sp. 1 0.44 0.698

Estuary x Fish sp. 4 25.20 0.001

Residual 40 1.23

There were two terrestrial plants (Suaeda australis and saltmarshes) that

were sampled in both the upper and lower sites of the Onkaparinga River, as were

epiphytes/periphyton and POM. The δ15N of S. australis, saltmarshes, and

epiphytes/periphyton was higher at the downstream site than the upstream site

(Fig. 4.3c). In comparison, the δ15N of POM was slightly lower at the downstream

site. The δ13C of S. australis, saltmarshes, epiphytes/periphyton, and POM were

all higher at the downstream site (Fig. 4.3c). Black bream and yellow-eye mullet

were more enriched in 13C at the downstream site, however their δ15N values did

not change between the two sites sampled.

139

Isotopic niche

The average δ15N range of black bream was 1.5 ± 0.8 (SD) ‰ with the largest

range being found in the Chapman River (see Table 4.3). The average δ15N range

for yellow-eye mullet was smaller at 1.2 ± 0.3 ‰, conversely the smallest range

was found in the Chapman River (see Table 4.3). The average isotopic niche

width (standard ellipse area, small sample size corrected, SEAc) of black bream

across estuaries sampled was 1.5 ± 0.8 (SD) ‰2 and the average for yellow-eye

mullet was 1.9 ± 1.4 (SD) ‰2. The mean of the Bayesian estimate of ellipse area

(SEAb) was larger for each estuary than the small sample size corrected ellipse

area (SEAc) except for yellow-eye mullet in the Harriet River where SEAb was

smaller (see Table 4.3). The isotopic niche width (measured by SEAc and SEAb)

of black bream was larger than yellow-eye mullet in the Chapman, Onkaparinga

lower and South West Rivers. The isotopic niche width (SEAc and SEAb) of

yellow-eye mullet was larger than that of black bream in the Harriet River and the

Onkaparinga upper site. However, there was no significant difference in standard

ellipse area (SEAc) between black bream and yellow-eye mullet across estuaries

(F1,9 = 0.23, p = 0.66). There was also no overlap between black bream and

yellow-eye mullet isotopic niches in any of the estuaries/sites sampled

(p_overlap < 0.001).

140

-35 -30 -25 -20 -15 -10-10

-5

0

5

10

15

20

δ13C

-35 -30 -25 -20 -15 -10-10

-5

0

5

10

15

20

Dist.

Salt.Seagrass

Epi.

POM

Marine macroalgae

Epi.

Macroalgae

GrassesMPB

MPBJuncus

Euc.

Sua.

Phrag.

Entro.

Lepto.

Shrubs

POM

c) d)

-35 -30 -25 -20 -15 -10δ15 N -10

-5

0

5

10

15

20

-35 -30 -25 -20 -15 -10-10

-5

0

5

10

15

20

Dist.Ruppia

MPBPlan.

Salt.

Mel.POM

Epi.

MPB

Mel.

POM

Epi.

MacroalgaeFic.

Juncus

a) b)

Amph.

141

Figure 4.3 Average δ15N and δ13C (‰ ± SE) of fish muscle and autotrophic

samples from a) Chapman, b) Harriet, c) Onkaparinga and d) South West rivers. Δ

= black bream; □ = yellow-eye mullet; grey fish samples are from the lower

Onkaparinga; ● = autotroph; grey circles are from the lower Onkaparinga. Amph.

= Amphibolis antarctica; Dist. = Disticus disticus; Entro. = Enteromorpha sp.;

Epi. = epiphytes/periphyton; Euc. = Eucalyptus sp.; Fic. = Ficinia nodusa;

Grasses = combined signature of two grasses; Juncus = Juncus sp.; Lepto. =

Leptospermum myrsinoides; Marine macroaglae/ Macroalgae = combined

signatures of several macroalga; Mel. = Melaleuca halmaturorum; MPB =

microphytobenthos; Phrag. = Phragmities australis; Plan. = Plantago coronopus;

POM = particulate organic matter; Ruppia = Ruppia sp.; Salt. = saltmarshes;

Seagrass = combined signature of Ruppia sp. and Zostera sp.; Shrubs = combined

signature of F. nodusa, D.disticus and Carptrobrotus rossi; Sua. = Suaeda

australis.

142

Table 4.3 Isotopic niche data for black bream (BB) and yellow-eye mullet (YEM)

in estuaries sampled in South Australia. δ15N range is the difference between the

smallest and largest values of δ15N for each species per estuary; SEAc is the

standard ellipse area corrected for small sample sizes; SEAb is the Bayesian

estimate of the ellipse area calculated as per Jackson et al. (2011).

δ15N range (‰) SEAc (‰2) SEAb (mean ‰2 ± SD)

Estuary BB YEM BB YEM BB YEM

Chapman 2.8 0.7 2.8 0.9 3.5 ± 1.8 2.2 ± 1.1

Harriet 0.7 1.5 1.0 4.1 2.6 ± 1.4 4.0 ± 2.0

Onkaparinga lower 1.7 1.1 1.8 1.1 2.8 ± 1.4 2.4 ± 1.2

Onkaparinga upstream 1.0 1.5 0.7 2.3 1.9 ± 1.0 3.4 ±1.7

South West 1.4 1.0 1.5 1.0 2.9 ± 1.5 2.0 ± 1.0

143

Autotrophic sources

Chapman River

Black bream in the Chapman River appear to rely most heavily on POM as a

source of nutrients, followed by epiphytes/periphyton, Melaleuca halmaturorum,

MPB, Disticus disticus, and saltmarshes (Fig. 4.4a). Plantago coronopus and

Ruppia sp. contributed very little nutrients to black bream diets.

As mentioned in the methods, the isotopic signatures of yellow-eye mullet

in the Chapman River did not fit well within the mixing polygon of sources when

trophically corrected at 1.5 TLs. Running SIAR with yellow-eye mullet at a

trophic level of two gave a better modelled result, although results were similar to

those obtained when using 1.5 trophic levels. Similar to black bream, yellow-eye

mullet, had high proportions of nutrients coming from D. disticus, POM,

epiphytes/periphyton, and MPB but with a much larger contribution from Ruppia

sp. (Fig. 4.4a). The autotrophs that contributed very little to yellow-eye mullet

dietary sources were P. coronopus, saltmarshes and M. halmaturorum.

144

2-24

8-35

0-26

0-29

0-20

0-15

0-24

0-16

0-25

0-28

4-33

3-33

0-21

0-25 0-

230-

18

Dis

t.

Epi

.

Pla

n.

Me

l.

MP

B

PO

M

Ruppia

Sal

t.0

5

10

15

20

25

30a)

Am

ph.

Epi

.

Fic

.

Juncus

Mac

roal

gae

Me

l.

MP

B

PO

M

0

5

10

15

20

25

30

0-15

4-31

1-29

0-24

1-31

0-21 0-

176-

33

0-27 1-

300-

19

0-26

0-26

1-26

0-19

0-21

b)

Epi

.

MP

B

PO

M

Sal

t.

Sea

gras

s

Sua.

0

5

10

15

20

25

30c)

0-20 0-

30

0-35

0-44

6-48

3-45

0-10 0-

13

0-36

0-360-36

0-38

Dis

t.

Epi

.

Euc.

Juncus

Phra

g.

PO

M

Sal

t.

Sua.0

5

10

15

20

25

30d)

0-10

6-25

0-16

3-39

0-18

2-38

0-20

1-29

1-33

1-19

0-30

0-23

0-22

0-29 0-

320-

20

Entr

o.

Epi

.

Gra

sses

Lepto

.

Mac

roal

gae

Mar

ine

mac

roal

gae

MP

B

PO

M

Shr

ubs

0

5

10

15

20

25

30

e)

0-22 1-

22 1-21

0-25

0-21 0-

240-

230-

260-

21

0-16

1-25

0-21

0-24

0-13

0-21

11-3

10-

13

0-22

Pro

porti

onal

con

tribu

tion

(%)

Autotrophic source

145

Figure 4.4 Proportional contributions of autotrophs to black bream (grey bars)

and yellow-eye mullet (white bars) diets (mode (95 % confidence intervals shown

above bars)) from SIAR analyses in the a) Chapman, b) Harriet, c) Onkaparinga

lower, d) Onkaparinga upstream, and e) South West rivers. See Fig. 4.3 for

autotrophic source groupings and abbreviations. Note: yellow-eye mullet results

for the Chapman River are from analyses with fish at a trophic level of two and in

the lower Onkaparinga yellow-eye mullet were analysed at a trophic level of one.

All other results are for yellow-eye mullet and black bream analysed at a trophic

level of 1.5 (see Results for further details).

146

Harriet River

In the Harriet River, reeds (Juncus sp. and Ficinia nodusa) and M. halmaturorum

contributed relatively high amounts of nutrients to black bream diets (modes >

18 %) (Fig. 4.4b). Conversely epiphytes/periphyton, MPB, POM, macroalgae, and

Amphibolis antartica all contributed very small amounts to black bream diets.

Yellow-eye mullet diets showed the opposite pattern with low proportions of

reeds (Juncus sp. and F. nodusa) and M. halmaturorum, but with high proportions

of A. antartica, epiphytes/periphyton, MPB, POM, and macroalgae (Fig. 4.4b).

Onkaparinga River

As mentioned in the methods, isotopic signatures from yellow-eye mullet in the

lower Onkaparinga did not fit well within the mixing polygon using 1.5 TLs.

However, running SIAR with yellow-eye mullet at TL = 1 gave similar results to

TL = 1.5. Although SIAR still flagged the results of the 1-TL analysis as

potentially being problematic, SIAR rated the problem as mild and the variance

associated with the model (SD2) went down from the model for 1.5-TL. We

believe we had sampled all available autotrophic sources in the area and as the

variance went down with only 1-TL, we present results from the 1-TL analysis.

In the lower reaches of the Onkaparinga black bream and yellow-eye

mullet rely on similar sources of nutrients. Most of their nutrients came from

saltmarshes, POM, MPB and S. australis, with very little nutrients coming from

epiphytes/periphyton and seagrass (Fig. 4.4c).

Upstream in the Onkaparinga epiphytes/periphyton were a major source of

nutrients for black bream, along with Phragmities autralis, S. australis and POM

(Fig. 4.4d). Saltmarshes, Juncus sp., Eucalyptus sp. and D. disticus contributed

relatively small amounts of nutrients to black bream diets. Yellow-eye mullet

147

nutrient sources were quite different from black bream in the upper Onkaparinga,

with large proportions of nutrients coming from Eucalyptus sp., saltmarshes,

D. disticus and Juncus sp. (Fig. 4.4d). Small fractions of yellow-eye mullet

dietary nutrients came from POM, S. australis, P. australis and

epiphytes/periphyton.

South West River

In the South West River, black bream received most of their nutrients from

Leptospermum myrsinoides, macroalgae, and „shrubs‟ (Fig. 4.4e). Nutrients from

Enteromorpha sp., epiphytes/periphyton, MPB and POM contributed

comparatively smaller amounts to black bream diets. Although L. myrsinoides

contributed large amounts to black bream diets, it contributed small amounts to

yellow-eye mullet diets. Yellow-eye mullet diets in the South West River

consisted of similar proportions of most sources sampled (Fig. 4.4e).

Similarity in autotroph reliance between black bream and yellow-eye

mullet

Black bream and yellow-eye mullet similarity of autotrophic reliance was more

than 50 % similar in the Chapman (Bray-Curtis similarity = 75 %), the lower

Onkaparinga (92 %) and South West rivers (70 %). The similarity of autotrophic

reliance in the Harriet River was much lower (40 %) and it was lowest in the

upper Onkaparinga (27 %).

Discussion

It was expected that isotopic niches of black bream and yellow-eye mullet might

overlap due to their shared environmental tolerances and omnivory. However, the

isotopic niches of black bream and yellow-eye mullet did not overlap in any of the

148

estuaries sampled. Although the autotrophs they rely on within these estuaries

were similar in some cases, they were quite different in other estuaries. The

significant difference between black bream and yellow-eye mullet isotopic

signatures in all estuaries reinforces that these two fishes have different isotopic

niches and potentially ecological niches.

The environmental dimensions of black bream and yellow-eye mullet

niches were expected to overlap as they are commonly found in the same

estuaries. If δ13C and δ15N adequately reflect environmental dimensions of habitat

use there should have been some overlap of isotopic niches between black bream

and yellow-eye mullet, yet our analyses found none. This may be due to the small

number of samples collected (n = 5), however Jackson et al. (2011) propose that

the methods we used to quantify isotopic niches (SEAc and SEAb) and their

overlap are not greatly affected by sample size. Black bream and yellow-eye

mullet were often caught in different areas within estuaries, suggesting that there

may be habitat separation or partitioning on a small spatial scale (Flaherty and

Ben-David, 2010; Pita et al., 2011) and stable isotope signatures may be reflecting

this (Janjua and Gerdeaux, 2011). Persistent habitat partitioning would be

supported by small movement of fish within an estuary and we found evidence to

support this. In the Onkaparinga River the difference in δ13C between upper and

lower sites suggested limited movement by fishes in this estuary, as consumer

signatures closely tracked that of autotrophs (Melville and Connolly, 2003;

Rasmussen et al., 2009). However, other researchers have found black bream to

move over much greater distances (Sakabe and Lyle, 2009), although these fish

were much larger than those sampled here (> 250 mm fork length). Also the sites

where black bream and yellow-eye mullet were caught together (lower

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Onkaparinga and South West River) were sites where fishes relied on similar

autotrophic sources, suggesting their habitats overlapped. Therefore the isotopic

niche separation between black bream and yellow-eye mullet may reflect spatial

separation of these two species within some estuaries on a small scale. This could

be tested by acoustically tagging individual fish and quantifying movements

simultaneously for both species, although the size of fish caught in this study may

be prohibitively small to attach tags to. If the drivers behind habitat partitioning

on such a small scale were to be ascertained, considerable work would need to be

done (e.g. determining distributions of each species at a scale relevant to habitat

characteristics, preference for different habitats, and determining if either species

defends habitats (Pita et al., 2011; Whitney et al., 2011)) and this would be very

challenging in the estuaries sampled.

The separate isotopic niches of black bream and yellow-eye mullet are

also likely to reflect differences in diets. Both fish species are known to be

omnivorous and to feed opportunistically on the most abundant prey (Sarre et al.,

2000; Platell et al., 2006), however if this were the case the two species would be

in direct competition with one another. It has been suggested that niche overlap

will be smallest when competition is most intense (Pianka, 1974). It could be that

competition is intense between black bream and yellow-eye mullet and this is why

we found no isotopic niche overlap. Indeed both black bream and yellow-eye

mullet are known to feed within and on the substratum as well as throughout the

water column (Sarre et al., 2000; Platell et al., 2006), suggesting that they would

be in direct competition with one another for food. Previous studies have found

little evidence of dietary overlap between black bream and yellow-eye mullet

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(Branden et al., 1974; Harbison, 1974, although these studies were in only one

estuary), further supporting the niche separation of the two species.

Although, we did not measure competition between black bream and

yellow-eye mullet we can analyse the sizes of the isotopic niches and use them as

an indicator to show how successful the fishes are as competitors (Olsson et al.,

2009). Black bream had a larger isotopic niche than yellow-eye mullet in three out

of the five estuaries/sites analysed suggesting that they are the better competitors.

However, yellow-eye mullet had a larger isotopic niche in two sites: the Harriet

River and the upper Onkaparinga. Both of these sites had different sizes of fish;

the Harriet River had larger yellow-eye mullet and the upper Onkaparinga had

smaller black bream than other estuaries. The larger yellow-eye mullet would be

able to take larger prey items (Platell et al., 2006; Stouffer et al., 2011) potentially

increasing their niche width. Conversely the smaller black bream in the upper

Onkaparinga would be restricted to smaller prey items and a smaller niche.

Therefore the ability of black bream and yellow-eye mullet to compete with one

another may be size dependent and isotopic niches may change with ontogenetic

changes in diet, as would their ecological niches (e.g. Hjelm et al., 2000; de la

Moriniere et al., 2003; Stouffer et al., 2011). Further research across a range of

fish sizes would be beneficial as ontogenetic changes in diets have been found

from stomach contents of black bream and yellow-eye mullet (Sarre et al., 2000;

Platell et al., 2006) and δ15N of a congener of black bream (A. australis) has been

found to vary with fish size (Melville and Connolly, 2003).

The range of δ15N for black bream and yellow-eye mullet was not greater

than the average isotopic discrimination for one trophic level (3.4 ‰, Post, 2002),

suggesting that the fishes are feeding within a trophic level and not spanning two.

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However, black bream had a δ15N range greater than half of the average trophic

discrimination (1.7 ‰) in two estuaries/sites. This range of half a trophic level

suggests that black bream range from being herbivores to omnivores in the lower

Onkaparinga and from omnivores to carnivores in the Chapman River. In the

lower Onkaparinga, there is a smaller difference between fish δ15N and autotroph

δ15N suggesting fish may be consuming more plant matter. Conversely in the

Chapman River there is a greater difference between fish δ15N and autotroph δ15N

suggesting black bream could be feeding at a higher trophic level. The largest

range in δ15N was recorded for black bream in the Chapman River (2.8 ‰) and

this was also the site of greatest size variation for black bream. Indeed there was a

positive correlation between variation in fish size and δ15N range for black bream,

but not for yellow-eye mullet. This is further evidence that more research is

needed into the relationship between fish size and δ15N of wild black bream in

particular.

Considering the isotopic niches of black bream and yellow-eye mullet

were different from one another and did not overlap: how similar was their

reliance on autotrophic sources? At the Onkaparinga lower site the similarity

between black bream and yellow-eye mullet autotroph reliance was at its highest

(92 %). The high similarity at this location may be due to the fact that the estuary

here is simplified into a relatively straight channel with little complex subtidal

habitat to partition between the two species. As fish are unlikely to partition this

habitat they are forced to compete for resources and yellow-eye mullet were

feeding at a lower trophic level than black bream and thus filling a different niche.

Black bream and yellow-eye mullet are being supported by similar autotrophs in

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the lower Onkaparinga as they are feeding at different trophic levels within the

food web.

Yellow-eye mullet in the Chapman River were feeding at a different

trophic level to black bream and this again may explain the high similarity in

autotroph reliance. Fishes in the South West River also had relatively high

similarity in autotroph reliance. The South West River was the only river that was

actually flowing at the time of sampling and therefore there may have been ample

nutrients available for both black bream and yellow-eye mullet such that there was

little need to compete (Milbrink et al., 2008; Chen et al., 2011). Further evidence

of lack of competition in the South West River is found in the condition of fishes

sampled. Fish collected in the South West River were in the best condition of all

fish for both species, suggesting a lack of competition (Milbrink et al., 2008).

Conversely fish were in the poorest condition in the Chapman River, where

competition appears to be high as black bream and yellow-eye mullet were

feeding at different trophic levels. The Chapman River is frequently bar-blocked

so competition in this estuary may be higher as there is little in-flow of nutrients

to stimulate productivity.

Low similarity of autotrophic sources between black bream and yellow-

eye mullet was found in the upper Onkaparinga (27 %). This part of the estuary

has a lot of structure to create microhabitats, where black bream and yellow-eye

mullet could partition habitats more readily. There had been little rain and

freshwater coming into the estuary prior to sampling, potentially making nutrients

less available and therefore forcing the two fishes to compete and utilise different

autotrophic sources/nutrients (Milbrink et al., 2008). In the Harriet River black

bream were caught in the lower layer of a salt-wedge incursion into the river and

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yellow-eye mullet were caught in the upper, fresher layer. This may explain why

the autotrophic sources the two fishes relied on were quite different, as they were

occupying different water bodies (Hjelm et al., 2000; Janjua and Gerdeaux, 2011).

The autotrophs that black bream relied on in the Harriet River were dominated by

terrestrially derived sources, suggesting that black bream were receiving nutrients

through detrital pathways in the lower layer of water. Yellow-eye mullet received

nutrients predominantly from marine and aquatic sources in the littoral zone,

suggesting they are using different resources and occupying different habitats to

black bream.

The data collected for this study represents only a snapshot in time and

repeated sampling over longer time periods, particularly over different seasons,

may provide insight into niche changes through time and with nutrient fluxes from

freshwater inputs. Alternatively sampling of multiple tissues from the same

individuals may elicit similar information, although discrimination factors for

tissues other than muscle for these two species have not yet been experimentally

quantified.

The results of this study show that two omnivorous fishes (black bream

and yellow-eye mullet), which are commonly found in the same estuaries, occupy

different isotopic niches and likely ecological niches. Previously, ecologists have

undertaken complex studies of numerous attributes to understand the different

niches co-habiting animals fill (e.g. Douglas and Matthews, 1992). Stable isotopes

may provide a more efficient means to study ecological niches and their potential

overlap, particularly with the advent of more advanced statistical approaches

(Jackson et al., 2011). Our data also shows that within a food web two omnivores

may receive nutrients from different autotrophs when competing with one

154

another, but may rely on similar autotrophs when feeding at different trophic

levels.

Acknowledgments

The Nature Foundation of South Australia provided funds to cover transport costs

to collect samples for this research. Additional funding was provided through an

ARC Linkage Grant (LP0669378) to B.Gillanders and T.Elsdon, and an ARC

Discovery Grant (DP0665303) to T.Elsdon. A.Bloomfield was supported by an

APA Scholarship, and B.Gillanders by an ARC Future Fellowship

(FT100100767). Fish were collected under Fisheries Management Act 2007

permit numbers 9902145 and 9902146 from the Department of Primary Industry

and Resources South Australia. The authors would like to thank Judith Giraldo for

her assistance with collecting samples and accommodation. Stable isotope

analyses were conducted by Rene Diocares at Griffith University. Many thanks go

to Andrew Jackson for his advice and timely responses to queries regarding the

use of SIAR and SIBER.

155

Chapter Five: Fish abundance and

recruitment show a subsidy-stress

response to nutrient concentrations

in estuaries

Western River opening to the sea beyond, Kangaroo Island, November 2011.

156

Chapter 5 Preamble

This chapter is a co-authored paper, with intention to publish in a peer-reviewed

scientific journal. Bronwyn Gillanders and Travis Elsdon are co-authors, therefore

it is written in plural.

In this chapter Travis Elsdon conceived the sampling design and secured

most of the funding. Bronwyn Gillanders assisted with funds. I assisted in

collecting the samples and prepared samples for stable isotope analyses. Travis

Elsdon prepared and analysed the otoliths and did the statistical analyses on

otolith data. I did the quantile regression spline analyses and wrote the manuscript

with input from Travis Elsdon and Bronwyn Gillanders.






157

Fish abundance and recruitment show a subsidy-stress

response to nutrient concentrations in estuaries

Abstract

Increased nutrients from human activities can have large effects on aquatic

ecosystems and may act in a subsidy-stress relationship with fish productivity. A

subsidy-stress affect may occur when low additions of nutrients increase

phytoplankton and subsequently fish production, due to increased food

availability. However, at high additions of nutrients fish production may decrease

due to degradation of habitat, such as seagrass and macroalgae. We assessed the

hypothesis that fish productivity will show a subsidy-stress response to nutrient

concentrations in estuaries by quantifying abundance and recruitment as

productivity measures. We measured abundance and recruitment of black bream,

Acanthopagrus butcheri, in estuaries in South Australia that varied in nutrient

concentrations. To determine recruitment, young-of-year fish were collected in

estuaries and their chemical otolith signatures determined. Subsequent year

classes (1+ and 2+ year old fish), originating from the young-of-year cohorts, were

assigned to their juvenile estuary from otolith analyses to determine recruitment

success. Nutrient concentrations of ammonia, oxidised nitrogen and

orthophosphorus were measured for each estuary in conjunction with fish

abundance. Stable nitrogen isotopes of fish muscle were analysed to determine

potential uptake of anthropogenic nutrients. Abundance and recruitment showed

subsidy-stress responses to nutrient concentrations, with peaks in black bream

abundance and recruitment occurring at low levels of nutrients. Fish with enriched

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nitrogen isotopes were found in estuaries with high ammonia concentrations.

Black bream abundance and recruitment peaked in estuaries with low

anthropogenic inputs of nutrients. Our observations are a basis on which future

work can build on to elucidate the mechanisms causing the subsidy-stress

response of black bream abundance and recruitment.

Introduction

Anthropogenic additions of nutrients to coastal waterways and ecosystems is a

global problem (Vitousek et al., 1997; Carpenter et al., 1998). Human-derived

additions of nitrogen and phosphorus lead to increased phytoplankton productivity

and decreased biomass of long lived aquatic plants and slow-growing macroalgae

(Cloern, 2001; Rabalais, 2002). This is a particular problem in estuaries as they

contain complex biogenic habitats of aquatic plants and macroalgae that are

known to support increased abundance of fish and invertebrates (e.g. Beck et al.,

2001; Bloomfield and Gillanders, 2005; Payne and Gillanders, 2009). Estuaries

and their complex biogenic habitats act as nurseries for juvenile fish (Beck et al.,

2001) where fish have increased growth and survival within complex habitats

(Heck et al., 2003; Minello et al., 2003). Estuaries often receive high

concentrations of nutrients through river systems that service large catchments.

Therefore estuaries, and their complex biogenic habitats, are important for fish

productivity but may be greatly affected by anthropogenic additions of nutrients.

Increases in nutrient concentration in aquatic systems may not always have

detrimental effects on fish productivity. Increased nutrients can „fertilise‟ aquatic

systems, stimulating primary production, particularly phytoplankton (Cloern,

2001; Capriulo et al., 2002; Rabalais, 2002). Increased primary production can

lead to increased availability of food for fish, which can result in higher growth

159

rates and better fish condition (Keller et al., 1990; Bundy et al., 2003; Milbrink et

al., 2008; González et al., 2010). Therefore some addition of nutrients may lead to

increased fish productivity through increased food availability. However,

excessive addition of nutrients to estuaries may lead to decreased fish productivity

when biogenic nursery habitats are lost or when water quality deteriorates, leading

to anoxia (Rabalais, 2002). This is an example of a subsidy-stress situation (Odum

et al., 1979) where increasing inputs of nutrients can lead to increasing outputs of

fish productivity from low to moderate levels (i.e. fish productivity is subsidised

by additional nutrients). However, at high levels of nutrients fish productivity is

diminished as biogenic habitats are lost and water quality deteriorates,

detrimentally affecting fish growth and survival (i.e. the system is stressed).

Fisheries productivity is commonly measured by biomass, however the

number of individual fish present and the proportion of fish that recruit

successfully (those that survive to be counted in following years) are also suitable

measures of fish productivity. Fish abundance and recruitment are measures of

productivity because they reflect survival and growth. We predict that if high

numbers of fish are present in estuaries with high nutrients it suggests that fish

productivity is stimulated by high nutrients, through increased growth and

survival. Conversely, if low numbers of individual fish are present in estuaries

with high nutrients it suggests that fish productivity has been suppressed due to

low survival and growth. If high nutrients detrimentally affects the proportion of

fish surviving to recruit into subsequent year classes (0 to 1+ or 2+ year old fish) it

will also have a detrimental effect on fish numbers in the future, and therefore

productivity. If high concentration of nutrients enhances recruitment it will also

enhance fish abundance and productivity in the future. Therefore measuring fish

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abundance and determining recruitment success among estuaries with different

nutrient concentrations will allow an assessment of the impacts of nutrients on

fish productivity.

Stable isotopes of nitrogen can be used to directly link anthropogenic

sources of nitrogen to fish and their food. Stable isotope ratios of nitrogen are

affected by human activities, with elevated ratios of 15N to 14N in compounds

formed by human activities compared to naturally occurring compounds that have

lower ratios (Heaton, 1986). The increase in δ15N (the ratio of 15N to 14N

expressed in relation to air as a standard) has been used to trace sewage inputs

through aquatic food webs (e.g. Gaston et al., 2004; Hadwen and Arthington,

2007). When an animal consumes a food item it assimilates the molecules from

that food item into its cells and takes on the δ15N of its food; thus the adage „you

are what you eat‟ (DeNiro and Epstein, 1981). The incorporation of nitrogen

stable isotopes from food sources into animal cells allows tracking of

anthropogenic nitrogen sources through elevated δ15N throughout food webs.

However, the heavy isotope of nitrogen, 15N, is preferentially retained over 14N

within animals during deamination of amino acids (Martínez del Rio et al., 2009

and references therein). Therefore δ15N also increases with increasing trophic

level in a food web (Minagawa and Wada, 1984) and the phrase has been adjusted

to „you are what you eat, plus a few parts per thousand‟ (the units of measure of

stable isotopes) (Fry, 2006). Stable isotopes of nitrogen present in fish tissue can

be used to determine trophic level and if anthropogenic inputs of nitrogen are

being taken up in a food web.

Black bream, Acanthopagrus butcheri, is a common estuarine sparid that

completes its life cycle entirely within estuaries in southern Australia (Potter and

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Hyndes, 1999; Norriss et al., 2002 and references therein). While there is evidence

of a subsidy-stress situation of freshwater flows and salinity stratification

affecting black bream spawning and recruitment success (Jenkins et al., 2010;

Sakabe et al., 2011), the research was undertaken in only two estuaries and there

has been no research into the effects of nutrients on black bream recruitment and

abundance. It is acknowledged that freshwater flows and nutrient concentration

are linked, however the processes by which they affect black bream recruitment

may be quite different. Salinity, as a result of freshwater flows, affects black

bream recruitment success directly through physiology, particularly eggs and

larvae (Haddy and Pankhurst, 2000). However, nutrient concentration may affect

black bream indirectly through food and habitat availability, which in turn affects

survival and growth. Larvae and juvenile black bream feed primarily on copepods

(Norriss et al., 2002 and references therein), which may have increased abundance

under high nutrient concentrations (Capriulo et al., 2002; Bundy et al., 2003).

Therefore productivity of black bream may be subsidised by anthropogenic

additions of nutrients through increased food availability for larvae and juvenile

fish. Juvenile black bream are known to have high abundance in seagrass and

macroalgal beds (Butcher, 1945; Norriss et al., 2002) where they may benefit

from increased growth or survival. However, seagrass and macroalgal cover may

be reduced under high nutrient conditions (Rabalais, 2002 and references therein)

potentially reducing black bream survival or growth. Therefore productivity of

black bream may be reduced under high or stressful additions of nutrients.

Although black bream complete their entire life cycle within estuaries they

can migrate among neighbouring estuaries (Butcher and Ling, 1962; Burridge et

al., 2004; Burridge and Versace, 2007), which is thought to be associated with

162

heavy freshwater flows (Chaplin et al., 1998; Potter and Hyndes, 1999). Therefore

black bream may form a metapopulation among neighbouring estuaries. Otolith

chemistry can be used to determine successful recruitment from individual

estuaries within a metapopulation by tracking individual fish to their juvenile

estuary (Elsdon et al., 2008). Otoliths are paired calcium carbonate (CaCO3)

structures within the inner ear of fish, primarily used for hearing and balance

(Popper and Lu, 2000). The ability of otoliths to track fish movements is based on

their accretion of new carbonate and protein material onto the outside surface of

the otolith on a daily basis (Campana, 1999). Incorporated within the carbonate

and protein are elemental impurities. The CaCO3 structure and elements are

preserved and are not subject to metabolic absorption, hence otoliths accurately

record a chemical chronology over the life-time of a fish. When fish living in

different environments incorporate different chemical concentrations into otoliths,

irrespective of the mechanism or reason behind the incorporation (Elsdon and

Gillanders, 2004; de Vries et al., 2005), then these chemicals may be used as a

habitat or location specific tag (Gillanders and Kingsford, 1996; Campana, 2005;

Elsdon et al., 2008). Thus, the chemicals in otoliths of black bream can be used to

determine recruitment success from different estuaries by tracking fish to juvenile

estuaries within a metapopulation (e.g. Gillanders, 2002).

We assessed the hypothesis that black bream productivity will show a

subsidy-stress response to nutrient concentrations by measuring abundance and

recruitment success of black bream in a cluster of neighbouring estuaries in South

Australia, Australia. We determined recruitment success by tracking 1+ and 2+

year old fish to juvenile estuaries using otolith chemistry. We measured nutrient

concentrations of estuaries to determine if black bream productivity is subsidised

163

at low to moderate levels of nutrients and stressed at high nutrients. These

measures also allowed us to estimate optimal nutrient conditions for black bream

abundance and recruitment. The δ15N of black bream muscle tissue was measured

from one cohort of young-of-year fish, in conjunction with nutrient analyses of

estuaries, to see if there is a link between anthropogenic additions of nitrogen and

fish productivity.

Methods

Study system, species, and collections

We sampled juvenile young-of-year (0+), 1+, and 2+ year old black bream and

measured nutrient concentrations in a group of temperate estuaries within South

Australia to determine which estuaries were more productive for black bream.

Twelve estuaries were sampled on the mainland and on a large offshore island,

Kangaroo Island (see Figure 5.1) over a three year period (2007-2009). These

estuaries represent a spatially discrete cluster of estuaries within the larger

distribution of black bream along the Australian coast, which spans from

Murchison River in Western Australia to Myall Lake in New South Wales

(Norriss et al., 2002). However, the estuaries sampled represent a group separated

by geographical breaks to the west and east of at least 100 km to the nearest open

estuary, which can result in genetic isolation due to the reduced migration habits

of black bream (Burridge et al., 2004; Burridge and Versace, 2007). We therefore

considered these estuaries to be a separate ecological population that may exhibit

local mixing of individuals among estuaries via movements of juvenile fish.

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Figure 5.1 Map of location of estuaries sampled in South Australia, Australia.

1 = Onkaparinga, 2 = Myponga, 3 = Carrickalinga, 4 = Bungala, 5 = Waitpinga,

6 = Hindmarsh, 7 = Chapman, 8 = Middle, 9 = Western, 10 = South West,

11 = Harriet, 12 = Eleanor.

Adelaide

Kangaroo Island

Australia

Enlarged area

South Australia

200 km

138ºE136ºE 137ºE

36ºSN

12

34

5

6

789

10 1112

165

To measure black bream abundance collections were taken seasonally

from summer/autumn 2007 to summer 2008, along with water samples for

nutrient analyses (see Water Sampling and Analyses for details). To determine

recruitment success fish were sampled in summer/autumn 2007, summer 2008,

and summer 2009. Although fish were sampled from all estuaries annually; not all

estuaries contained populations of 0+ fish in 2007. In 2007 no 0+ fish were caught

in the Onkaparinga, Myponga, Carrickalinga, or Hindmarsh estuaries; which

suggest either poor spawning, settlement of larvae or poor recruitment of fish.

Estuaries in which no 0+ fish were caught in 2007 contained 1+ and 2+ fish from

previous years‟ 0+ population in 2008 and 2009.

Within each estuary, 0+ fish (n = 16 fish) were collected in 2007 and 2008

and 1+ and 2+ fish (n=28-122) were also collected in subsequent years (2008: 1+,

2009: 1+ and 2+) to link back to the 0+ fish from 2007 and 2008. Fish were

collected using seine nets. Collections were done along the entire length of

estuaries, and estuaries were not sub-sampled at multiple sites to assess within

estuary variation (e.g. Gillanders, 2005) because estuaries were generally small in

length (2.5 ± 1.5 km, mean ± SE; range 0.39 -11.02 km) and approximately 5-

100 m wide. All fish were aged to confirm they were young-of-year (0+) fish, 1+

or 2+. Recruitment was assessed based on the numbers of 1+ and 2+ fish found in

the metapopulation in subsequent years from estuaries with characterised otolith

signatures for 0+ fish.

Otolith preparation and analysis

Sagittal otoliths of fish were dissected, washed, and cleaned in deionised water,

and allowed to air dry in microcentrifuge tubes under a laminar flow cabinet. One

otolith from each fish was embedded in epofix resin (Struers) that had been spiked

166

with 40 ppm indium to allow for discrimination between the otolith matrix and

resin upon analysis. Otoliths were sectioned transversely through the focus (centre

section) using a low-speed diamond saw (Buehler Isomet) and polished to 200 -

300 µm thickness using lapping film (Elsdon and Gillanders, 2002). Polished

sections were fixed to glass slides with thermoplastic glue that was spiked with

indium resin (CrystalBond 509) and allowed to dry. Once the glue was dry slides

were sonicated in ultrapure water and dried again before analysis. Slides were

stored individually in plastic bags.

The concentrations of elements (Sr, Ba, Ca, Mn, Mg, Li, and Zn) in otolith

samples were determined using a New Wave 213 nm UV laser connected to an

Agilent 7500cs inductively coupled plasma-mass spectrometer (ICP-MS). Laser

ablations occurred inside a sealed chamber with the sample gas being extracted to

the ICP-MS via a smoothing manifold in the presence of argon and helium gas.

The chamber was purged for approximately 10 s after each ablation to remove

background gas from previous ablations (Lahaye et al., 1997). The laser operating

conditions were similar to Munro et al. (2008): frequency, 5 Hz; laser spot size,

30 µm; laser power, 65 %; beam energy ~ 0.08 – 0.12 mJ; carrier gas, Ar

(0.87 L min-1); with ICP-MS operating conditions of: optional gas, He (57.5 %),

dwell times, Ca43 (50 ms), Sr88, Ba138, Mn55, Mg24, Li7, Zn66 (all 200 ms), In115

(100 ms). Background concentrations of elements within the chamber were

measured before each ablation (for 25 s), to allow for correction of sample

concentrations.

A spot on the outside edge of otoliths of 0+ fish was analysed in order to

quantify chemical concentrations of newly incorporated material from juvenile

estuaries. A spot on the edge of the first growth band of otoliths from 1+ and 2+

167

fish, which corresponded to the same location as that analysed for 0+ fish, was

also analysed to determine juvenile chemical signals. Otoliths were analysed in

several sampling sessions with two reference standards to allow for comparisons

among sampling sessions. A reference standard (National Institute of Standards

and Technology, NIST 612) was analysed after every 12 otolith ablations to

correct for machine drift (Ludden et al., 1995). The concentrations of elements in

otoliths were corrected using a fish otolith standard (a 32 mm pressed powdered

disk of finely ground otolith, similar to that described by Fallon et al., 1999 for

coral), which was analysed at the beginning and end of each sample session.

Analytical accuracy determined from the concentrations of the NIST standards

and averaged across all samples was 100 % for Ca, Sr, 101 % for Ba, Mg, Mn, Li,

and 103 % for Zn. Detection limits were assessed as 3 standard deviations above

the blanks that were run during analyses, and were < 0.0005 μmol.mol-1 for Sr,

Ba, Mg, Mn, Li, Zn, and < 0.004 μmol.mol-1 for Ca. All otolith values were above

detection limits. All data reduction was done off-line in spreadsheets and

consisted of smoothing, background subtracting, standardising to NIST 612,

normalising to calcium (internal standard for ablation yield), and correcting to the

fish standard.

Water sampling and analyses

Water samples were collected seasonally from summer/autumn 2007 to summer

2008 from estuaries on the mainland of Australia (Onkaparinga, Myponga,

Carrickalinga, Bungala, Waitpinga and Hindmarsh Rivers, see Fig. 5.1). Water

samples were also collected from estuaries on Kangaroo Island (Chapman,

Middle, Western, South West, Harriet, and Eleanor Rivers, see Fig. 5.1) in winter

2007 and summer 2008. Black bream were collected for abundance measures at

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these times, to enable black bream abundance to be related to nutrient

concentrations. Two replicate water samples were taken from nine sites along

each estuary with three sites in three sections of estuaries; the upper, middle and

lower sections of each estuary. As estuaries varied in length it was necessary to

divide estuaries into relevant sections, with the upper section being at the

headwaters, or area of lowest salinity; the lower sections being at the estuary

mouth or closest to the sea; and the middle section being halfway between the

upper and lower sections. Sites within sections were separated by at least 30 m,

with replicate water samples taken approximately 5 m apart. Black bream were

also sampled for abundance at these sites using a seine net over an area of

approximately 25 m2.

Water samples were collected using a 25 mL syringe, which was

submerged to the depth of the syringe (10 cm) so that samples represented surface

water samples. Water was then filtered through a 0.45 μm membrane filter into a

15 mL polypropylene screw-top vial. Samples were stored on ice in the field, and

immediately frozen at − 20°C until analysis. Prior to samples being analysed they

were defrosted (within 2 h of analysis) and placed in racks on the auto analyser

stage. Nutrients were analysed using an Automated Ion Analyser (QuickChem

8500 FIA), using standard methods of oxidised nitrogen (NO-2/3: nitrite/nitrate

(NOx); QuickChem® Method 31-107-04-1-A), orthophosphate (PO43−; Method

31-115-01-1-I), and ammonia (NH3; Method 31-107-06-1-B). The lower limits of

oxidised nitrogen concentration measures were considered to be 0.001 mg N/L.

Stable isotope analysis

The δ15N of fish muscle was measured to determine if anthropogenic sources of

nutrients were being taken up into the food web and potentially affecting black

169

bream productivity. Dorsal muscle samples were taken from YOY fish collected

in 2008, as YOY black bream were caught in all estuaries that year. Muscle

samples from ten fish, where possible (all estuaries except Myponga), from each

estuary were freeze-dried and ground into a powder using an agate mortar and

pestle. Samples were weighed into tin capsules for δ15N analysis. Samples were

analysed by a GV Isoprime Mass Spectrometer coupled to a Eurovector elemental

analyser 3000 at Griffith University, Queensland, Australia. International and

internal laboratory standards (Ambient Air, IAEA-305a, Acetanilide, Working

standard: 'Prawn') were run in parallel with fish muscle samples to enable

calibration of results. Average precision of the mass spectrometer was 0.18 ‰

(1SD), with average accuracy of 0.18 ‰ (average deviation from known value).

Statistical analyses

Otolith chemistry analysis and classification

To determine whether there were significant differences of multi-element

signatures among estuaries for 0+ fish in each of 2007 and 2008 permutational

multivariate analysis of variance (PERMANOVA, Anderson, 2001) was used.

Data were ln(x+1) transformed and resembled using Euclidean distance similarity

matrices. Permutations were done on residuals under the reduced model. Post-hoc

comparisons, (Anderson, 2001) were used to determine which estuaries differed in

otolith signatures.

Quadratic Discriminant Function Analysis (QDFA) does not assume

homogeneity of covariance matrices and tolerates modest deviations from

normality (McGarigal, 2000). Therefore QDFA was used, with a jackknife cross-

validation procedure to determine classification accuracy. QDFA was used to

determine classification success of fish to their estuary of known origin for 0+

170

fish. Classification tests were done for individual estuaries, and when chemical

tags of fish were similar among estuaries classification tests were done using a

combination of individual estuaries and groups of estuaries.

To determine the proportion of recruitment to the metapopulation from

each estuary elemental signatures of otoliths for 0+, 1+, and 2+ yr old fish from

2007, 2008 and 2009 respectively were compared. Elemental signatures of

otoliths from 0+ and 1+ fish from 2008 and 2009 respectively were also compared

using the Maximum Likelihood Estimation (MLE) program HISEA (Millar,

1990). HISEA computes the likelihood of observing the measurements made on

an individual given that it is from a particular group: i.e. the likelihood of a fish

with measured elemental signatures (1+ and 2+ yr old fish) coming from an estuary

with characterised elemental signatures (0+ fish of the respective year). HISEA

was run in bootstrap mode without re-sampling of standards (n = 16 fish/estuary),

although mixtures were re-sampled with varying numbers of fish caught in each

year/age group (2008 1+ fish n = 28; 2009 2+ fish n = 39; 2009 1+ fish n = 122).

The program calculated standard deviations of the contribution of each estuary by

re-sampling the mixed stock data 100 times with replacement.

Recruitment, abundance and nutrients

We analysed the relationship of black bream productivity (abundance and

recruitment) with nutrient concentrations (ammonia (NH3), oxidised nitrgoen

(NOx), orthophosphorus (P)) using quantile spline regressions (quantile spline

regressions encapsulate a set proportion of data points below the line (Koenker,

2005)) to find the nutrient concentrations at which recruitment and abundance

peaked. Black bream abundance data were single measures of abundance taken at

the same time as water samples (see above). If no black bream were recorded at

171

sites where water samples were taken, points were omitted as the absence of black

bream may be due to factors other than nutrient concentrations. Black bream

recruitment data were the percent classification of 1+ and 2+ fish back to juvenile

estuaries. We therefore had to average nutrient data over the entire estuary to

relate nutrient concentrations during juvenile development to estuarine-scale

recruitment.

Two sets of analyses were done to relate nutrient concentrations of

estuaries to black bream recruitment, as there were no water samples collected

from estuaries on Kangaroo Island in summer/autumn 2007. One set of analyses

used recruitment data from estuaries on the mainland for fish that were young-of-

year in 2007 (2008 1+, 2009 2+), paired with nutrient data from summer/autumn

2007. Recruitment data from all estuaries for 2008 0+ (2009 1+) fish were also

used in these analyses, pairing them with nutrient data from summer 2008. These

data sets were paired together as fish would have experienced these nutrient

concentrations before high winter rainfall and freshwater flows when they may

leave the estuary. The second set of analyses were done assuming no inter-annual

variation in nutrient concentrations within estuaries and used summer 2008

nutrient data with all corresponding recruitment contributions per estuary. Doing

this enabled us to use all the recruitment data gathered. We acknowledge that no

inter-annual variation in nutrient concentrations is an assumption, however, we

believe it is feasible due to the local climate being relatively consistent across

years. Summer rainfall is extremely low in South Australia, therefore minimal

nutrients from surrounding land would be washed into estuaries over this time

period. Groundwater tables in summer should also be fairly stable, or decreasing

as it is dry. Similar levels of nutrients were found between summer/autumn 2007

172

and summer 2008 in estuaries that we had data for. The only differences in some

estuaries were increased ammonia concentrations in the summer and decreased

oxidised nitrogen and orthophosphorus compared to autumn.

Quantile regression spline models were done to encapsulate 95 % of the

data below the spline and were done in the R statistical environment (R

Development Core Team, 2011). The function “rq” was used (as part of the

“quantreg” package Koenker, 2007) and combined with “bs” (part of the “splines”

package, see Hastie, 1992) to fit piecewise polynomials of specified degrees

(similar to Anderson, 2008). The small sample correction version of Akaike‟s

information criterion (AICc) was used to determine the appropriate number of

parameters for the piecewise polynomials. The model with the lowest AICc value

having polynomial degree = 3, 4 or 5 was chosen as the best fit for the data. If two

models had AICc values within 2 units of each other (and so could be deemed the

same due to parsimony (Burnham and Anderson, 2002)), the model with the better

visual fit or more accurate confidence intervals for peaks was chosen. The nutrient

concentrations at which black bream abundance and recruitment peaked were

determined from the regressions. To determine the 95 % confidence intervals of

peaks, we calculated bias corrected percentiles by re-applying the model to each

of 10,000 bootstrapped sample pairs using the appropriate polynomial per data set

and restricting the range of nutrients to the first peak of the regression curve.

We further investigated the relationship of δ15N values of black bream

muscle and nutrient concentrations (NH3 and NOx only). We expected that high

concentrations of ammonia and oxidised nitrogen would be due to anthropogenic

influences and therefore δ15N of black bream muscle would also be high,

suggesting a positive correlation. We also graphed black bream abundance and

173

recruitment against δ15N to show that elevated nutrient inputs from anthropogenic

sources were affecting black bream abundance and recruitment.

Results

Otolith chemistry and classification to juvenile estuary

Elemental signatures of otoliths differed among most estuaries for the two years

of 0+ fish sampled (PERMANOVA: 2007 F7,112 = 7.704, p = 0.0002; 2008 F10,154

= 10.576, p = 0.0002). In 2007, eight estuaries contained 0+ black bream. Of those

estuaries, all fish except those from Harriet and South West estuaries had different

otolith signatures. By grouping the Harriet and South West estuaries together, the

minimum classification accuracy was 73 % (increased from 67 % when estuaries

were separate), and the average classification success was 85.1 % (Table 5.1).

174

Table 5.1 Summary of classification accuracies of young-of-year (0+) black

bream back to their juvenile estuary from Quadratic Discriminant Function

Analysis for fish collected in 2007 and 2008. Justification for groupings of

estuaries is based upon PERMANOVA results.

Classification success of 0+ black bream to juvenile estuary

Estuary/Grouping 2007 2008

Onkaparinga no fish 100 %

Myponga no fish too few fish to classify

Carrickalinga no fish 80 %

Bungala 87 % 77 %*

Waitpinga 93 % 93 %

Hindmarsh no fish *

Chapman 93 % 93 %

Middle River 80 % 80 %

Western River 73 % 76 %#

South West 90 %† #

Harriet † 80 %

Eleanor 80 % #

Groupings † = South West/

Harriet

* = Bungala/ Hindmarsh

# = Western River/South

West/Eleanor

175

In 2008, a total of 11 estuaries contained 0+ black bream that could be used

in analyses. Although Myponga estuary was sampled extensively in 2008, only

two 0+ black bream could be found and were therefore excluded from analyses. Of

the 11 estuaries, there were two groupings that had similar otolith signatures: the

Bungala and Hindmarsh; and the Western River, Eleanor, and South West. The

remaining estuaries all differed in elemental signatures (Table 5.1). Classification

accuracies for estuaries/groups ranged from 100 % in Onkaparinga to 76 % in the

Western River/Eleanor/South West group with average classification success of

84.9 % (an increase from 79.3 % when ungrouped; Table 5.1).

Of the estuaries that had 0+ fish collected from them in 2007, the Eleanor

River and South West/Harriet group contributed by far the greatest proportions of

recruits to 1+ and 2+ age groups (Table 5.2). The Bungala, Waitpinga, and

Chapman estuaries contributed small proportions of recruits to the overall

metapopulation (≤ 15 %), with no individuals classified to the Middle or Western

River. The Western River/South West/Eleanor group contributed the most recruits

of 1+ fish to the metapopulation of 2009 (see Table 5.2). The Bungala/Hindmarsh

group contributed the second highest proportion of recruits to the metapopulation,

with very small proportions (< 8 %) being identified from other estuaries.

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Table 5.2 Proportional contribution of recruitment to the metapopulation

estimated for 1+ and 2+ year old black bream per estuary/group based on otolith

chemistry. Maximum Likelihood Classification estimator results: average %

(± SD).

2007 0+ fish 2008 0+ fish

Estuary/Grouping 2008 1+ fish 2009 2+ fish 2009 1+ fish

Onkaparinga - - 3 % (± 2)

Myponga - - -

Carrickalinga - - 1.2 % (± 1)

Bungala 6.7 % (± 6) 6 % (± 6) 20.3 %* (± 5)

Waitpinga 0 % (± 0) 2 % (± 3) 2.6 % (± 2)

Hindmarsh - - *

Chapman 0 % (± 0) 15 % (± 7) 2.7 % (± 2)

Middle River 0 % (± 0) 0 % (± 0) 1.8 % (± 2)

Western River 0 % (± 0) 0 % (± 0) #

South West 38.2 %† (± 8) 44 %† (± 6) 60.8 %# (± 5)

Harriet † † 7.6 % (± 4)

Eleanor 55.1 % (± 12) 33 % (± 9) #

Total 100 % 100 % 100 %

Groupings † = South West/ Harriet * = Bungala/ Hindmarsh

# = Western River/South

West/Eleanor

177

Black bream abundance and recruitment related to nutrient concentrations

Black bream abundance and recruitment showed subsidy-stress responses to

increased concentrations of ammonia, oxidised nitrogen and orthophosphorus

(Fig. 5.2). Black bream abundance and recruitment increased with increasing

ammonia and orthophosphorus concentrations at low levels (Figs 5.2 a, c, d, f, g

and i). However, at high levels of ammonia black bream abundance and

recruitment were reduced. At high levels of orthophosphorus black bream

abundance was reduced, however recruitment appeared to be increasing again,

although this is driven by few data points. Black bream abundance and

recruitment peaked at very low levels of oxidised nitrogen concentrations and

decreased sharply thereafter (Figs 5.2 b, e and h). Both nutrient data sets showed

similar trends for recruitment analyses (Figs 5.2 d to i), with accuracy of peaks in

recruitment and nutrient concentrations varying between data sets (Table 5.3).

Black bream abundance peaked at ammonia concentrations of 0.012 mg

N/L, with recruitment peaking at 0.031-0.036 mg N/L (depending on which data

set was used; see Figs 5.2a, d, g, and Table 5.3). Black bream abundance peaked

at the lower limits of oxidised nitrogen concentration detection (0.001 mg N/L)

and recruitment peaked at approximately 0.01 mg N/L (see Table 5.3). Black

bream abundance and recruitment also peaked at low levels of orthophosphorus

concentration (approximately 0.01 mg P/L).

178

0.0 0.4 0.80

20406080

0.0 0.4 0.80

20406080

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0

200

400

600

800

1000

Abu

ndan

ce (n

o. o

f fis

h)

a) Abundance & Ammonia

0.0 0.2 0.4 0.6 0.8 1.0

0

200

400

600

800

1000b) Abundance & Oxidised Nitrogen

0.00 0.02 0.04 0.06 0.08 0.10

0

200

400

600

800

1000c) Abundance & Orthophosphorus

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0

20

40

60

80

Rec

ruitm

ent (

% o

f pop

n.)

d) Recruitment & Ammonia

0.00 0.02 0.04 0.06 0.08 0.10

0

20

40

60

80 e) Recruitment & Oxidised Nitrogen

0.00 0.02 0.04 0.06 0.08 0.10

0

20

40

60

80 f) Recruitment & Orthophosphorus

0.00 0.05 0.10 0.15 0.20 0.25 0.30

0

20

40

60

80

Ammonia (mg N/L)

Rec

ruitm

ent (

% o

f pop

n.)

g) Recruitment & Ammonia(assuming no inter-annual variability)

0.00 0.02 0.04 0.06 0.08 0.10

0

20

40

60

80

Oxidised nitrogen (mg N/L)

h) Recruitment & Oxidised Nitrogen(assuming no inter-annual variability)

0.00 0.02 0.04 0.06 0.08 0.10

0

20

40

60

80

Orthophosphorus (mg P/L)

i) Recruitment & Orthophosphorus(assuming no inter-annual variability)

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Figure 5.2 Abundance (number of fish caught in seines over approx. 25 m2; a), b), and c)) and recruitment (% of population; d), e), f). g), h), and

i)) of black bream related to concentration of nutrients: ammonia (mg N/L; a), d), and g)), oxidised nitrogen (nitrite/nitrate; mg N/L; b), e), and

h)), and orthophosphorus (mg P/L; c), f), and i)). Nutrient measures paired with abundance data are averages of two water samples taken at the

same location as black bream, collected seasonally over a year along estuaries (data are not included when black bream were absent). Nutrient

measures for recruitment graphs are averages of water samples taken along the length of the estuary, or group of estuaries, for mainland estuaries

in summer/autumn 2007 and all estuaries in summer 2008 (d), e), and f)) corresponding to cohorts that grew in those estuaries as young of year;

and using nutrient samples collected in summer 2008 only (g), h), and i)). Recruitment estimates were calculated by Maximum Likelihood

Estimation of which estuary 1+ and 2+ year old fish recruited from using otolith chemistry data. Quantile spline regressions are shown for the 95th

percentile with peaks in black bream abundance and recruitment, as calculated from the models, indicated by vertical lines (see Table 5.3 for

specifics of regression analyses). Note the scale of graphs e) and h) are one tenth of that of b), shown in full as small inset graphs.

180

Table 5.3 Estimated optimal nutrient concentrations (ammonia = NH3, oxidised nitrogen (nitrite/nitrate) = NOx, orthophosphorus = P) for black

bream abundance and recruitment (and 95 % confidence intervals) from quantile regression splines (see Fig. 5.2), with polynomial degree

indicated. Nutrient data set used are noted. Note: *Rounded up to 0 as the lower limit; **Lower detection limit of machine.

Productivity

measure

Nutrients data set Nutrient Polynomial

degree

Estimated optimal nutrient

concentration (mg N or P per L)

95 % CI

Abundance Seasonal water samples collected

from summer/autumn 2007 to

summer 2008

NH3 5 0.012 (0*, 0.026)

NOx 5 0.001** (0*, 0.330)

P 5 0.010 (0.008, 0.038)

Recruitment Summer/autumn 2007 for mainland

estuaries and summer 2008 data for

all estuaries

NH3 4 0.031 (0.005, 0.079)

NOx 4 0.008 (0.006, 0.020)

P 4 0.011 (0.002, 0.022)

Summer 2008 data for all estuaries NH3 3 0.036 (0*, 0.074)

NOx 4 0.011 (0.004, 0.034)

P 3 0.013 (0.005, 0.025)

181

Nitrogen stable isotopes of fish tissue and nutrient concentrations

There was a positive linear relationship between δ15N of black bream muscle and

ammonia concentration of estuaries (r2=0.56; see Fig. 5.3a), with the slope being

significantly different from zero (p < 0.001). However there was no relationship

between δ15N of black bream muscle and oxidised nitrogen concentration of

estuaries (r2=0.22; see Fig. 5.3b), with the slope being not significantly different

from zero (p = 0.13). The positive relationship between δ15N of black bream

muscle and ammonia concentration was strongly influenced by high values of

both in the Onkaparinga.

Black bream abundance and recruitment peaked in estuaries with low δ15N

of black bream muscle (see Fig. 5.4), this relationship appears to be strongly

driven by data from the Onkaparinga. The peaks in black bream abundance and

recruitment are shown for illustrative purposes only in Fig. 5.4 as the data sets

were too small to obtain sensible confidence intervals.

182

Figure 5.3 δ15N of young of year (0+) black bream muscle (‰; mean ± SE) and a)

mean ammonia and b) mean oxidised nitrogen concentration of estuarine waters

(mean mg N/L ± SE) for twelve estuaries sampled in summer 2008. Linear

regression lines shown with equations and r2 details given.

0.00 0.05 0.10 0.15 0.20 0.25 0.30

5

10

15

20

Ammonia (mg N/L)

δ15 N

fish

mus

cle

(‰)

y = 8.6 + 25.5x

r2 = 0.56

a)

0.00 0.02 0.04 0.06 0.08 0.10

5

10

15

20

Oxidised nitrogen (mg N/L)

y = 8.8 + 67x

r2 = 0.22

b)

183

Figure 5.4 δ15N of black bream muscle tissue (‰; mean ± SE) from young of

year (0+) fish caught in summer 2008 and a) abundance of black bream in

estuaries in summer 2008 (total number of fish caught per estuary) and b)

recruitment per estuary/group of estuaries (% of population tracked to different

estuaries/groups) of fish caught in summer 2009 (1+ yr old fish). Solid lines show

quantile regression splines of degree three. Peaks in black bream abundance and

recruitment occur at δ15N = 8.76 ‰ shown by dashed lines. Confidence intervals

for peaks in abundance and recruitment could not be obtained due to too few data

points.

10 12 14 16

0

50

100

150

200

250

300

350

δ15N fish muscle (‰)

Abu

ndan

ce (n

o. o

f fis

h)

a)

10 12 14 16

0

20

40

60

80

100

δ15N fish muscle (‰)

Rec

ruitm

ent (

% o

f pop

ulat

ion)

b)

184

Discussion

Black bream productivity showed a subsidy-stress response to nutrient

concentrations in estuaries. Black bream abundance and recruitment increased

with increasing nutrient concentrations at low levels of ammonia, oxidised

nitrogen and orthophosphorus. However, at medium to high levels of nutrients

black bream abundance and recruitment were detrimentally affected and

decreased dramatically. The high levels of ammonia in estuaries appear to be

caused by anthropogenic influences as δ15N of fish tissue increased with

increasing ammonia concentration of estuaries.

Significant differences in otolith chemistry were found among most

estuaries for juvenile young-of-year baseline otolith signatures. Some estuaries

had to be grouped together, however, which was required as it increased

classification accuracies of 0+ fish and was subsequently needed for classification

of 1+ and 2+ fish to juvenile estuaries. Estuaries with similar otolith signatures

either shared adjacent watersheds or had similar geology. South West and Harriet

Rivers both have deposits of sandstone along the estuary and had similar otolith

signatures in 2007. However, in 2008 their otolith signatures were significantly

different, with South West River grouping out with Western and Eleanor Rivers.

This may be due to environmental factors influencing otolith chemistry, such as

salinity and temperature (Elsdon and Gillanders, 2002), which can vary due to

rainfall and bar-blocking of estuaries. Unfortunately we do not have detailed

climatic data available on estuarine flows, rainfall, or bar-blocking over the

sampling period to correlate with groupings of otolith signatures. Bungala and

Hindmarsh estuaries had similar otolith signatures in 2008, and although they are

spatially separated, both drain glacial and fluvioglacial deposits. The similarity of

185

otolith chemistry signatures among estuaries may be due to similar geology and/or

similar environmental conditions (Barnett-Johnson et al., 2008).

The proportion of the metapopulation that each estuary contributed varied

between years, although some estuaries contributed small numbers of black bream

to both cohorts (< 5 %: Onkaparinga, Carrickalinga, Waitpinga, and Middle

Rivers). The estuaries that were grouped together contributed the largest

proportions of recruits to the metapopulation (Harriet & South West Rivers, 2007

0+; Western, South West, & Eleanor Rivers, Bungala & Hindmarsh Rivers, 2008

0+). The grouping of estuaries for otolith signatures may have encompassed more

variability in otolith chemistry allowing more fish to be classified to the groups.

However, South West, Eleanor, and Harriet Rivers were always among the groups

or individual estuaries contributing high proportions of recruits. These high

contributions of recruits indicate the importance of these estuaries as spawning

and nursery grounds for black bream and show that they act as sources of black

bream for the metapopulation (Gillanders, 2002; Hamer et al., 2005).

The strong recruitment of black bream from groups of estuaries drove the

subsidy-stress responses observed for recruitment and nutrient concentrations.

Regardless of which nutrient data set was used for the recruitment analyses, both

sets showed similar subsidy-stress responses with peaks found at similar nutrient

concentrations. Therefore we believe that assuming no inter-annual variation in

nutrient concentrations was reasonable. There were also subsidy-stress responses

observed for black bream abundance and nutrient concentrations. These subsidy-

stress responses of black bream abundance and recruitment suggest that small

increases in nutrient concentrations may increase growth and survivorship.

Increased survivorship of black bream is shown by the high numbers of fish

186

collected in estuaries with low additions of nutrients, as well as high recruitment

from those estuaries. Increased growth at low nutrient concentrations may be

reflected by increased recruitment of fish to the metapopulation from those

estuaries with low nutrient additions. If additions of nutrients lead to increased

food abundance at low levels, fish may grow faster and larger in these estuaries

(Keller et al., 1990; Bundy et al., 2003). Body size has been found to be a major

influencing factor on an individual‟s ability to successfully disperse (Benard and

McCauley, 2008). Therefore if fish can grow larger and faster in estuaries with

low additions of nutrients they can disperse further, and potentially sooner, and be

more numerous in the metapopulation than fish that grow in estuaries with high

additions of nutrients, where growth rates may be slower. Growth rates may be

slower in estuaries with high nutrient additions for several reasons; one of which

is that other planktivorous fishes may dominate the fish assemblage and out-

compete black bream leading to slower growth and decreased survival.

Comparing the growth rates of black bream and the amount of food available to

fish among estuaries was beyond this study, but is needed to further our

understanding of the mechanisms causing the peaks in abundance and recruitment

at low nutrient concentrations.

Black bream abundance and recruitment were suppressed at high nutrient

concentrations. High additions of nutrients can lead to decreased biomass of long

lived aquatic plants and slow-growing macroalgae (Cloern, 2001; Rabalais, 2002),

suggesting that black bream may need these biogenic habitats as juvenile areas.

There is some evidence of higher abundance of black bream in seagrass and

macroalgae (Butcher, 1945; Norriss et al., 2002), however we did not quantify the

extent of these habitats within the estuaries studied and therefore can only

187

speculate that the aerial extent of these habitats may vary among estuaries.

Although it seems likely that black bream may have increased survival and

growth in biogenic habitats (Heck et al., 2003) this has not specifically been

quantified for this species. More extensive research is needed to understand the

mechanisms that are causing black bream abundance and recruitment to be

suppressed at high nutrient concentrations.

We found a positive relationship between ammonia concentration of

estuarine waters and δ15N of muscle from fish living in those waters. The

relationship is strongly influenced by high values of both ammonia and δ15N from

the Onkaparinga. The Onkaparinga was the only urban estuary sampled, with the

remainder being within rural catchments. There were waste water sludge lagoons

situated next to the Onkaparinga at the time of sampling that were known to flood

into the Onkaparinga occasionally and were possibly leaching into the estuary.

Therefore the high ammonia concentration is likely caused by human influences,

which also causes high δ15N of nitrogen compounds (Heaton, 1986). Although we

did not measure the δ15N of ammonia or dissolved inorganic nitrogen in estuarine

waters, the 15N of ammonia in the Onkaparinga is probably enriched (Heaton,

1986). As the 15N-enriched ammonia, and other anthropogenically derived

nitrogen containing compounds, are taken up by plants and algae the entire food

web is enriched in 15N. Indeed enriched 15N of plants and algae has been recorded

in the Onkaparinga (Chapter 4). This scenario is much more realistic than juvenile

black bream feeding at a higher trophic level in the Onkaparinga, as all fish

analysed for δ15N were the same age and a similar size. We also found that sites

with high fish abundance and recruitment had low δ15N of fish muscle. This

188

indicates that the estuaries with high black bream abundance and recruitment were

also estuaries with low human impacts.

Estuaries are naturally variable, and perhaps stressful, environments. It has

been suggested that due to the high variability of estuarine environments

organisms that live in estuaries are particularly well adapted to environmental

variability (Elliott and Quintino, 2007). Elliott and Quintino (2007) further argued

that we should assess functional characteristics of estuaries instead of structural

characteristics, such as biodiversity, because high environmental variability is

likely to lead to decreased biodiversity and structure of ecosystems. Here we have

assessed the function of black bream recruitment from estuaries to a

metapopulation and it has shown a subsidy-stress response with nutrient

concentrations. The subsidy-stress response of this function further supports the

hypothesis that organisms inhabiting estuaries are well adapted to environmental

variability, as black bream recruitment occurred even at high nutrient

concentrations although it was somewhat diminished. The adaptability of black

bream and other estuarine organisms may obscure our ability to detect

anthropogenic impacts in estuaries, as they can adapt and persist in highly

variably environments (Elliott and Quintino, 2007).

Conclusion

We found that black bream productivity showed a subsidy-stress relationship with

nutrient concentrations and that the increase in nutrient concentrations is probably

due to human influences. However, as we have only focused on nutrient

concentrations we cannot rule out the affects of other water quality parameters,

such as dissolved oxygen, salinity, and heavy metals, and their effects on black

bream recruitment and abundance (Breitburg et al., 1999a; Breitburg et al.,

189

1999b). Our observations suggest that the mechanisms behind the subsidy-stress

response of black bream abundance and recruitment to nutrient concentrations

warrant further investigation.

Acknowledgments

We wish to thank people who assisted with collection of black bream and

preparation of samples, including Chris Izzo, Judith Giraldo, Ruan Gannon,

Benjamin Walther, Patrick Reis Santos, Noël Diepens, and Marthe deBruin. The

project was funded from an ARC Discovery grant and Fellowship (DP0665303)

to Travis Elsdon and an ARC Linkage grant (LP0669378) to Bronwyn Gillanders

and T. Elsdon. B. Gillanders was supported by an ARC Future Fellowship

(FT100100767) while this manuscript was written. We acknowledge Rene

Diocares from Griffith University for doing the stable isotope analyses.

191

Chapter Six: General Discussion

Aquarium set up for feeding experiments on yellow-eye mullet.

Darling Aquarium room, University of Adelaide.

192

General Discussion

Stable isotopes have become a commonly used tool in ecological research. They

can help us decipher food web interactions, migratory paths, and track our impact

on the environment. However, the discrimination of stable isotopes varies among

animals and tissue types and this may lead to erroneous results of field studies.

Environmental influences, such as temperature and diet composition, can also

affect discrimination and these effects need to be accounted for to aid

interpretations. Consequently there have been calls for experimental

determination of discrimination factors for individual species and further

investigations into the causes of variation in discrimination. This thesis

investigated variation in discrimination factors and applied experimentally derived

discrimination factors to field investigations to improve determination of

autotrophic sources.

Temperature effects

Most organisms experience temperature variation throughout their life and

seasonal variation in temperature can have large affects on organisms.

Temperature affected both tissue turnover rates and discrimination factors of δ13C

and δ15N (Chapters 2 and 3). Fish reared at warmer temperatures generally had

faster tissue turnover rates and smaller discrimination values than fish reared at

colder temperatures. This was largely due to kinetic effects on chemical reactions

and diffusion, where molecules with the heavier isotope move slower at colder

temperatures and so are less involved in chemical reactions (Dawson and Brooks,

2001). This results in fewer molecules with the heavier isotope being taken up

193

into animal tissue from the diet at colder temperatures. These results largely agree

with other published studies (e.g. Bosley et al., 2002; Witting et al., 2004).

Tissue turnover of isotopes occurs through both growth and metabolism,

which are affected by temperature (Chapters 2 and 3, Fry and Arnold, 1982;

Hesslein et al., 1993; Herzka et al., 2002). Growth is generally considered to be

the main process contributing to isotope turnover of muscle in growing

poikilotherms (Fry and Arnold, 1982; Bosley et al., 2002; Witting et al., 2004;

Trueman et al., 2005; Carleton and Martínez del Rio, 2010) and fish reared at

warmer temperatures generally grew faster than fish reared at colder temperatures

(Chapters 2 and 3). As an animal grows and accretes new tissue it uses nutrients

from recently consumed food to build that new tissue. Through metabolism,

absorbed food is catabolised for energy and some is used for tissue replacement

and maintenance. Therefore, in growing animals, growth will contribute more to

changes in isotope ratios through dilution effects than metabolism where most

food is burnt for energy (Karasov and Martínez del Rio, 2007). Results from

Chapter 2 further support this. In Chapter 2, δ15N of black bream muscle did not

change greatly over 42 days for fish reared at 16°C, but they did change for fish

reared at 23°C which were growing faster. These results suggest that metabolism

alone may be responsible for tissue turnover at colder temperatures as fish reached

equilibrium sooner at 16°C than at 23°C (Chapter 2). Considering the variable

growth rates of fish with temperature, isotopic signatures of fish may reflect their

diets only during warmer growth periods (Chapters 2 and 3, Perga and Gerdeaux,

2005; Carleton and Martínez del Rio, 2010).

Discrimination factors were larger at colder temperatures than at warmer

temperatures for δ15N of both black bream and yellow-eye mullet (Chapters 2

194

and 3). However, the effect of temperature on discrimination of δ13C was

dependent on the diet fish were fed in both experiments. The diets with lower C:N

ratios (fish-meal feed and chicken) had higher proportions of protein and fish fed

these diets had larger δ13C discrimination at warmer temperatures than at colder

temperatures. Fish fed diets with higher protein content may have catabolised

more protein from their diet for energy, compared with those fed a diet with lower

protein content, allowing fish to store more lipids (Karasov and Martínez del Rio,

2007). However, at warmer temperatures these lipids are also metabolised or not

formed through increased metabolism. Conversely at colder temperatures fish

store lipids and do not metabolise them causing fish δ13C to be more negative with

increasing lipid content (DeNiro and Epstein, 1977; Post et al., 2007). Fish muscle

C:N ratios support this (Chapter 3). Animal condition and C:N ratios of tissue are

closely related (Kaufman and Johnston, 2007) and other research has found

discrimination to vary with ration intake (Barnes et al., 2007), which also likely

influences animal condition.

Diet effects

They say “you are what you eat” and to a certain degree „isotopically‟ fish are,

however, what constitutes their diet may have complex affects on isotopic

signatures. Diet quality, or C:N ratios, appears to have a strong influence on tissue

turnover rates and discrimination. In Chapter 3, I found that the poor diet quality

of Artemia restricted the growth of yellow-eye mullet, which may have resulted in

little change in δ15N of muscle tissue. This may be due to fish metabolising lipids

during short starvation periods instead of using protein, as others have found

starvation caused δ15N to increase (Hobson et al., 1993; Gaye-Siessegger et al.,

2004b; Kelly and Martínez del Rio, 2010). Starvation/fasting can be divided into

195

three phases (Karasov and Martínez del Rio, 2007). In phases one and two lipids

are used for energy and protein catabolism is reduced. However in stage three

proteins are catabolised (Karasov and Martínez del Rio, 2007) and this is probably

when δ15N increases, as 14N is preferentially excreted as a product of protein

catabolism and no new nitrogen is consumed. Therefore, fish fed the low quality

diet of Artemia were likely sparing their dietary protein leading to lower

discrimination of δ15N and little change in δ15N over time (Chapter 3, Guelinckx

et al., 2007).

Further evidence to support the idea of protein being spared from

catabolism on poor quality diets is found in the δ13C of tissues. In Chapter 3,

although δ15N did not change greatly for yellow-eye mullet fed Artemia over time,

δ13C did change. Yellow-eye mullet fed Artemia increased in δ13C over time. This

may be due to Artemia having a higher δ13C signature than worms, but it also may

be confounded by fish burning 13C-depleted lipids leading to a further increase in

δ13C. Fish fed Artemia were in poor condition with those reared at 24°C being in

the worst condition, and having the lowest C:N ratios and the highest δ13C,

suggesting they have burnt off lipids and are using all carbohydrates consumed for

metabolism (Karasov and Martínez del Rio, 2007).

Turnover of δ15N was affected by the magnitude of difference in δ15N

between the swapped diets. The differences in δ15N between the hatchery diet and

the fish-meal feed (Chapter 2), and between worms and Artemia (Chapter 3) were

smaller than the differences between the baseline feeds (hatchery diet and worms)

and the other diets used (vegetable feed and chicken) in the experiments. This

created different turnover rates between diets (Chapters 2 and 3). In both

experiments fish changed from diets with high δ15N to diets with lower δ15N and

196

this may have shown a slower turnover or elimination of 15N within fish tissue; as

opposed to switching from a low-δ15N-diet to high-δ15N-diet (uptake). If 15N is

already incorporated into tissue protein, then it may be more difficult to eliminate

as 15N forms stronger molecular bonds than 14N, leading to slower

elimination/turnover rates (MacNeil et al., 2006). However, if an animal has been

fed a low-δ15N-diet and is then switched to a high-δ15N-diet, it may take 15N up

faster as 15N may be more readily able to displace 14N.

Although compound-specific isotope analyses can provide us with insights

into animal nutrition, physiology, and ecology (e.g. Chapter 2, Chikaraishi et al.,

2007; Hannides et al., 2009; Lorrain et al., 2009) the analyses themselves are

relatively expensive and time consuming and therefore may be restricted in their

application to field studies. Elemental concentration is easily measured and is

routinely provided when analysing stable isotopes, with most mass spectrometers

having elemental analysers attached to them. In Chapter 3, I investigated the

importance of elemental concentration in determining isotopic signatures of

animal tissue. Indeed, accounting for elemental concentration improved

predictions of muscle tissue isotopic signatures when using mixing models

(Chapter 3). Correlations between isotopic signatures of fish muscle and

elemental concentration of diet were also found. However, no relationship was

found between isotopic discrimination and elemental concentration or C:N ratios,

in contrast to others (Adams and Sterner, 2000; Kelly and Martínez del Rio,

2010). Adams and Sterner (2000) found a positive correlation between δ15N of

Daphnia magna and C:N ratios of its diet (Scenedesmus acutus). They also found

that the discrimination of δ15N by D. magna was positively correlated to the C:N

ratios of its diet. The pivotal differences between their experiment and Chapter 3

197

is that they used the one diet source and manipulated the C:N ratios of the algae,

analysing whole D. magna. In contrast, two different diet sources were mixed in

Chapter 3 to obtain different C:N ratios and muscle tissue was analysed.

Therefore in the experiment by Adams and Sterner (2000) the D. magna would

have fed on a diet of similar constituents (in terms of amino acids, lipids, and

other essential nutrients), however, in Chapter 3 yellow-eye mullet diets likely

varied in constituents as well as C:N ratios and this may have resulted in isotopic

routing (Kelly and Martínez del Rio, 2010).

Ecological applications

Stable isotopes of carbon and nitrogen can provide useful insights into the ecology

of systems that are more challenging to study traditionally, such as estuaries.

Estuaries are complex ecosystems, often comprised of various habitats, and can be

difficult to study due to water turbidity, among other factors. Estuaries are also

one of the most heavily impacted environments in the world (Kennish, 2002).

Black bream abundance and recruitment showed subsidy-stress responses to

increased concentrations of nutrients, with peaks in abundance and recruitment

occurring at very low nutrient concentrations (Chapter 5). Human impacts may be

traced by anthropogenic enriched nitrogen isotopes, particularly sewage impacts,

through to black bream in estuaries (Gaston and Suthers, 2004; Hadwen and

Arthington, 2007). Animal wastes and sewage mainly contain nitrogen in the form

of urea which is hydrolysed to ammonia. Some of the ammonia escapes as gas

and this gas is strongly depleted in 15N, leaving behind an enriched 15N ammonia

in solution (Heaton, 1986). Therefore water bodies with sewage inputs will have

high ammonia concentrations and high δ15N. A positive linear relationship

between ammonia concentration of estuarine waters and δ15N of black bream

198

muscle tissue was found (Chapter 5), showing that ammonia was taken up into the

food web. This relationship was strongly influenced by data from the Onkaparinga

River, where high values of both ammonia concentration and δ15N of black bream

muscle were recorded. The Onkaparinga had sewage sludge lagoons adjacent to it

at the time of field sampling. These sludge lagoons were known to flood into the

river occasionally and may still be leaching contaminants through ground water

inputs, even though they have since been decommissioned. Chapter 5 showed that

these sludge lagoons were likely having an impact on the estuarine ecosystem of

the Onkaparinga as black bream abundance and recruitment were suppressed,

although the mechanisms leading to lower abundance and recruitment need

further investigation.

It has been suggested that niche overlap will be smallest when competition

is most intense (Pianka, 1974). In the lower Onkaparinga I found high similarity

in autotroph reliance between black bream and yellow-eye mullet, despite no

overlap in niches. However, fish were caught in the same area, where habitat was

simplified and potentially somewhat difficult to defend (Chapter 4). It is possible

that in the lower Onkaparinga competition between black bream and yellow-eye

mullet was intense and that this had forced yellow-eye mullet to feed at a lower

trophic level than black bream, thus occupying a different niche. The

anthropogenic influences in this estuary may have simplified the ecosystem

somewhat, causing more competition between fish (González-Ortegón et al.,

2010). In the South West River, which is mostly surrounded by National Park,

high similarity in autotrophs between the fishes and no niche overlap were also

found. In contrast to the Onkaparinga, I suggested that sufficient food was

available for both fishes in the South West River such that competition was

199

reduced. Both species were in good condition in the South West River, the best

condition of all estuaries sampled, suggesting competition was reduced and that

the ecosystem was thriving (Chapter 4, Milbrink et al., 2008; Chen et al., 2011).

The finding of no overlap in isotopic niches, and potentially ecological niches, of

black bream and yellow-eye mullet could be because competition within estuaries

has resulted in black bream and yellow-eye mullet occupying separate niches and

this may be influenced by anthropogenic impacts on the entire ecosystem.

Black bream were enriched in 15N over yellow-eye mullet in the

Onkaparinga whereas they were 15N-depleted in all other estuaries (Chapter 4).

The potentially high concentration of 15N-enriched ammonia in the Onkaparinga

may be taken up by black bream directly, increasing δ15N of fish muscle. Moeri et

al. (2003) found that both an ammonotelic and ureotelic fish took up 15N-enriched

ammonia at the cellular level, although at different rates, when held in 15N-

enriched ammonia solution. In their experiment the ureotelic fish was not as

enriched in 15N as the ammonotelic fish and they suggested this may be due to its

active ornithine-urea cycle which enables it to rapidly sequester ammonia away

from the circulatory system to the liver, reducing the exchange of 15N-enriched

ammonia with muscle tissue (Moeri et al., 2003). Although most bony fishes are

ammonotelic, some can be ureotelic (McDonald et al., 2006). It may be that

yellow-eye mullet can be ureotelic and therefore are able to reduce the amount of

ammonia being taken up from the water at the cellular level. In contrast, black

bream are likely ammonotelic and not able to prevent uptake of ammonia,

resulting in black bream being more 15N enriched than yellow-eye mullet in the

Onkaparinga. This is an alternative explanation, as opposed to feeding at a higher

200

trophic level, as to why black bream were enriched in 15N over yellow-eye mullet

in the Onkaparinga (Chapter 4).

If black bream are ammonotelic and yellow-eye mullet are ureotelic, it

may also explain the differences in turnover rates of δ15N between the two species

(Chapters 2 and 3). Experimental fish were not reared in flow through systems

and therefore excreted ammonia was held in tanks for up to 48 hrs. If black bream

are ammonotelic, they may have taken up excreted ammonia δ15N from within

tanks and therefore shown slower turnover rates. Conversely, if yellow-eye mullet

are ureotelic they would not have taken up dissolved ammonia from within tanks

and would have had faster turnover rates. The differences in physiology of fishes

may have affects on experimental and food web interpretations using δ15N if the

physiology of fish (i.e. ammonotelic or ureotelic) is not known and 15N-enriched

ammonia is present.

Future directions

Since the initial call for more experiments on stable isotopes in animals

(Gannes et al., 1997) the field of stable isotope research has progressed somewhat,

but new innovations are needed. The most popular application of stable isotopes is

to determine diets, however diet quality can affect discrimination and

subsequently isotopic signatures. Without being able to determine the

relationships between diet quality (Chapters 2 and 3), ration intake (Barnes et al.,

2007), nutritional status of animals (Hobson et al., 1993; Gaye-Siessegger et al.,

2004b), and the consequences on stable isotope signatures we may come to

erroneous conclusions when it comes to dietary and food web studies. Future

research into relationships between animal condition and stable isotopes may

benefit field studies as we cannot always quantify diet quality or ration intake in

201

the field. Research into enzyme activity may also help us understand variation in

isotopic discrimination and nutritional status of wild animals, by indicating which

metabolic processes are dominating, and therefore improve dietary back-

calculation (Gaye-Siessegger et al., 2005).

There may be generalities in tissue turnover rates for animals within

taxonomic groupings that are similar in size or growth pattern. I found a similar

muscle tissue turnover rate of δ15N for yellow-eye mullet (27.2 days half life) to

that of the sand goby Pomatoschistus minutus (27.8 days half life, Guelinckx et

al., 2007). The fish in both studies were of similar size and tissue turnover rates

were quantified for the respective fish‟s ambient summer temperatures (24°C for

yellow-eye mullet and 17°C for the sand goby). Therefore a review of the

literature may discover patterns in tissue turnover rates with animal size and

temperatures experienced by animals in nature.

Although only elimination rates of isotopes were quantified in this thesis,

others have quantified uptake and elimination of δ15N (MacNeil et al., 2006).

MacNeil et al. (2006) found variable uptake and elimination of δ15N in a stingray,

Potamotrygon motoro, with the initial uptake of 15N being much faster than

elimination in several tissue types (liver, blood, cartilage, and muscle). In nature it

is more likely that an animal will switch from a diet low in δ15N to one that is

higher, as it grows and moves up the food chain, because 15N is enriched with

every trophic level (Minagawa and Wada, 1984). Thus, it would make more sense

to aim to quantify the uptake rates of 15N in the future to obtain more relevant

turnover rates.

To improve field studies using stable isotopes it may be better to analyse

homogenised samples of entire animals in the future, so as to eliminate the

202

possibility of isotopic routing skewing results. It is apparent that particular

constituents of diets are routed or directed to certain tissues and therefore the

isotopic signatures of those tissues are more similar to the particular diet

constituents (e.g. bone apatite δ13C reflects that of food catabolised for energy

Ambrose and Norr, 1993; muscle tissue more closely reflects δ13C of dietary

protein Kelly and Martínez del Rio, 2010). Kelly and Martínez del Rio (2010)

point out that mixing models do not account for isotopic routing, and indeed to do

so would be extremely complicated. Therefore, it may be more realistic to aim to

analyse homogenised samples of entire animals (where appropriate) than to

account for isotopic routing in field studies.

Although I did not find a correlation between fish size and δ15N for black

bream or yellow-eye mullet directly, there was evidence that size of black bream

may influence δ15N. There was a positive correlation between δ15N range and fish

size variation for black bream, but not for yellow-eye mullet (Chapter 4). Within

any one estuary the range of yellow-eye mullet size was small, and this is

probably because it is a schooling species. Therefore finding a similar relationship

between δ15N range and fish size variation for yellow-eye mullet was unlikely in

Chapter 4, although it may occur in nature. Future studies should try to sample a

larger range in fish sizes per estuary to determine if there are relationships of fish

size and niche width with δ15N of fish tissue.

To further investigate habitat partitioning between black bream and

yellow-eye mullet, fish abundance needs to be quantified at different spatial scales

and surveys repeated at different places within estuaries over time. However,

finding adequate sampling gear and methodologies may be challenging in the

estuaries sampled due to complex habitats, making it difficult to catch fish

203

without influencing abundance measures. Acoustic tagging of both fishes within

the same area may also help determine if habitat partitioning is occurring,

although this method may only be applicable to fish larger than those collected in

this study due to the size of the tags.

Conclusion

Using stable isotopes in ecology often involves accounting for discrimination,

however discrimination factors applied in field studies are often grand means

across many species or are from related organisms. These bulk discrimination

factors fail to acknowledge that discrimination can vary among animals, as well as

within animals. Here I have begun to answer the calls for more experiments on the

causes of variation in discrimination factors and tissue turnover rates (Gannes et

al., 1997; Robbins et al., 2005; Martínez del Rio et al., 2009). I found that

temperature and diet affected discrimination factors and tissue turnover rates.

However our ability to predict temperature conditions and diet quality

experienced by fish in the wild prior to collection is limited. Future research into

relationships of fish condition and enzyme activity with stable isotopes may help

improve estimates of discrimination and consequently field study interpretations. I

found evidence to suggest that ammonia was being taken up at the cellular level,

by black bream in particular, and this may affect experimental and field data

interpretations. Stable isotopes of carbon and nitrogen will continue to be used in

ecology and although some progress has been made, new innovations in

experimental research are needed.

204

Appendix A: Permission to republish Chapter Two

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Licensed content publication Journal of Experimental Marine Biology and Ecology

Licensed content title Temperature and diet affect carbon and nitrogen isotopes of fish muscle: can amino acid nitrogen isotopes explain effects?

Licensed content author Alexandra L. Bloomfield,Travis S. Elsdon,Benjamin D. Walther,Elizabeth J. Gier,Bronwyn M. Gillanders

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Appendix B: Supplementary data for Chapter Two

Average δ15N ± SE ‰ of individual amino acids for all treatment groups analysed.

Diet Hatchery feed Vegetable feed Fish-meal feed

Temperature 16°C 23°C 16°C 23°C 16°C

Day 0 7 14 28 42 42 42 42

Alanine 26.96 ± 0.76 27.77 ± 1.00 26.38 ± 1.32 25.51 ± 0.96 25.06 ± 0.55 24.90 ± 0.77 25.81 ± 0.74 25.30 ± 0.75

Glycine 6.46 ± 0.81 7.73 ± 0.33 6.02 ± 0.62 5.18 ± 0.68 5.02 ± 0.20 4.54 ± 0.71 4.82 ± 0.67 4.81 ± 0.67

Threonine -12.94 ± 1.03 -11.37 ± 1.13 -11.69 ± 1.18 -11.82 ± 1.53 -12.20 ± 1.19 -14.55 ± 1.17 -13.87 ± 0.91 -14.03 ± 0.85

Valine 7.47 ± 0.79 9.03 ± 0.81 7.69 ± 0.33 7.40 ± 1.27 6.33 ± 0.30 5.81 ± 0.17 6.62 ± 0.97 5.39 ± 1.07

Serine 22.26 ± 1.37 23.64 ± 1.41 23.38 ± 1.13 22.37 ± 0.88 23.07 ± 0.70 20.66 ± 0.10 22.94 ± 0.84 23.64 ± 1.97

Leucine 25.71 ± 1.06 25.43 ± 0.88 24.78 ± 1.17 23.54 ± 1.50 23.56 ± 0.73 24.59 ± 0.23 24.95 ± 0.14 25.62 ± 1.08

Isoleucine 24.98 ± 1.10 25.14 ± 0.82 25.40 ± 1.11 25.85 ± 0.46 23.69 ± 1.03 24.84 ± 0.76 23.95 ± 0.62 24.58 ± 1.14

Proline 21.82 ± 0.51 20.90 ± 0.78 21.12 ± 0.62 19.95 ± 0.87 20.16 ± 0.90 20.22 ± 1.58 20.09 ± 0.45 21.21 ± 0.41

Aspartic acid 24.13 ± 0.78 22.34 ± 0.78 22.33 ± 0.80 21.16 ± 2.25 21.76 ± 0.88 22.69 ± 0.49 22.41 ± 0.18 23.19 ± 0.64

Glutamic acid 26.21 ± 0.95 25.48 ± 0.89 26.06 ± 0.64 24.29 ± 1.41 23.88 ± 0.93 24.58 ± 0.28 25.54 ± 0.19 26.26 ± 0.62

Phenylalanine 8.94 ± 0.56 7.93 ± 0.89 8.62 ± 0.90 10.34 ± 0.66 8.52 ± 0.67 9.13 ± 1.71 8.38 ± 0.38 9.64 ± 1.00

Lysine 9.75 ± 0.53 9.22 ± 0.52 9.67 ± 0.34 7.56 ± 2.39 8.91 ± 0.69 8.90 ± 0.80 8.60 ± 0.33 9.13 ± 0.28

207

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